Results matching “R”

Reihan Salam discusses a theory of Tyler Cowen regarding "threshold earners," a sort of upscale version of a slacker. Here's Cowen:

A threshold earner is someone who seeks to earn a certain amount of money and no more. If wages go up, that person will respond by seeking less work or by working less hard or less often. That person simply wants to "get by" in terms of absolute earning power in order to experience other gains in the form of leisure.

Salam continues:

This clearly reflects the pattern of wage dispersion among my friends, particularly those who attended elite secondary schools and colleges and universities. I [Salam] know many "threshold earners," including both high and low earners who could earn much more if they chose to make the necessary sacrifices. But they are satisficers.

OK, fine so far. But then the claim is made that "threshold earning" behavior increases income inequality. In Cowen's words:

The funny thing is this: For years, many cultural critics in and of the United States have been telling us that Americans should behave more like threshold earners. We should be less harried, more interested in nurturing friendships, and more interested in the non-commercial sphere of life. That may well be good advice. Many studies suggest that above a certain level more money brings only marginal increments of happiness. What isn't so widely advertised is that those same critics have basically been telling us, without realizing it, that we should be acting in such a manner as to increase measured income inequality [emphasis added]. Not only is high inequality an inevitable concomitant of human diversity, but growing income inequality may be, too, if lots of us take the kind of advice that will make us happier.

This is a cute idea but I don't think it's correct. I'll explain my reasoning but first one more quote from Salam:

Type S error: When your estimate is the wrong sign, compared to the true value of the parameter

Type M error: When the magnitude of your estimate is far off, compared to the true value of the parameter

More here.

Since this blog in November, I've given my talk on infovis vs. statistical graphics about five times: once in person (at the visualization meetup in NYC, a blog away from Num Pang!) and the rest via telephone conferencing or skype. The live presentation was best, but the remote talks have been improving, and I'm looking forward to doing more of these in the future to save time and reduce pollution.

Here are the powerpoints of the talk.

Now that I've got it working well (mostly by cutting lots of words on the slides), my next step will be to improve the interactive experience. At the very least, I need to allocate time after the talk for discussion. People usually don't ask a lot of questions when I speak, so maybe the best strategy is to allow a half hour following the talk for people to speak with me individually. It could be set up so that I'm talking with one person but the others who are hanging out could hear the conversation too.

Anyway, one of the times I gave the talk, a new idea came out: One thing that people like about infovis is the puzzle-solving aspect. For example, when someone sees that horrible map with the plane crashes (see page 23 of the presentation), there is a mini-joy of discovery at noticing--Hey, that's Russia! Hey, that's India! Etc. From our perspective as statisticians, it's a cheap thrill: the reader is wasting brainpower to discover the obvious. But I think most people like it. In this way, an attractive data visualization is functioning like a Chris Rock routine, when he says something that we all know, but he says it in such a fresh new way that we find it appealing.

Conversely, in statistical graphics we use a boring display so that anything unexpected will stand out. It's a completely different perspective. I'm not saying that statisticians are better than infovis people, just that we strive for different effects.

Another example are those maps that distort the sizes of states or countries to be proportional to population. Everybody loves these "cartograms," but I hate 'em. Why? Because the #1 thing these maps convey is that some states on the east coast have high population density and that nobody lives in Wyoming. People loooove to see these wacky maps and discover these facts. It's like being on vacation in some far-off place and running into Aunt Louise at the grocery store. The shock of the familiar.

(I'm not opposed to all such maps. In particular, I like the New York Times maps that show congressional and electoral college results within stylized states that include congressional districts as little squares. These maps do the job by focusing attention on the results, not on the cool processes used to create the distortions.)

Malecki's right, this is very cool indeed.

P.S. Is it really true that "4.5 million Parisians" ride the Metro every day? Even setting aside that not all the riders are Parisians, I'm guessing that 4.5 million is the number of rides, not the number of people who ride.

Many years ago, a research psychologist whose judgment I greatly respect told me that the characterization of personality by the so-called Big Five traits (extraversion, etc.) was old-fashioned. So I'm always surprised to see that the Big Five keeps cropping up. I guess not everyone agrees that it's a bad idea.

For example, Hamdan Azhar wrote to me:

In an article headlined, "Hollywood moves away from middlebrow," Brooks Barnes writes:

As Hollywood plowed into 2010, there was plenty of clinging to the tried and true: humdrum remakes like "The Wolfman" and "The A-Team"; star vehicles like "Killers" with Ashton Kutcher and "The Tourist" with Angelina Jolie and Johnny Depp; and shoddy sequels like "Sex and the City 2." All arrived at theaters with marketing thunder intended to fill multiplexes on opening weekend, no matter the quality of the film. . . .

But the audience pushed back. One by one, these expensive yet middle-of-the-road pictures delivered disappointing results or flat-out flopped. Meanwhile, gambles on original concepts paid off. "Inception," a complicated thriller about dream invaders, racked up more than $825 million in global ticket sales; "The Social Network" has so far delivered $192 million, a stellar result for a highbrow drama. . . . the message that the year sent about quality and originality is real enough that studios are tweaking their operating strategies. . . . To reboot its "Spider-Man" franchise, for instance, Sony hired Marc Webb, whose only previous film was the indie comedy "(500) Days of Summer." The studio has also entrusted a big-screen remake of "21 Jump Street" to Phil Lord and Chris Miller, a pair whose only previous film was the animated "Cloudy With a Chance of Meatballs." . . . Guillermo del Toro, the "Pan's Labyrinth" auteur, is developing a new movie around Disneyland's Haunted Mansion ride. . . .

"In years past," said Sean Bailey, Disney's president for production, "most live-action films seemed like they had to be either one thing or the other: commercial or quality. The industry had little expectation of a film being both. Our view is the opposite."

Huh? Standards have certainly changed when a Spiderman sequel, and a 21 Jump Street remake, and a ride at Disneyland are defined as "highbrow."

The cultural products described in the article--big-money popular entertainments that are well-reviewed and have some association with quality--are classic middlebrow. Back around 1950, Russell Lynes and Dwight Macdonald were all over this.

Of course, Lynes and Macdonald would've identified the New York Times as Middlebrow Central and so wouldn't have been surprised at all to see uber-middlebrow items labeled as highbrow. That's the whole essence of middlebrow: to want the "qualiity" label without putting in the work. 21 Jump Street, indeed.

P.S. I agree with (the ghosts of) Lynes and Macdonald that these middlebrow movies are just fine if that's what people want. It's just funny to see them labeled as "highbrow," in what almost seems like a parody of middlebrow aspiration. So "edgy."

Appointed "inflation czar" in late 1970s, Alfred Kahn is most famous for deregulating the airline industry. At the time this seemed to make sense, although in retrospect I'm less a fan of consumer-driven policies than I used to be. When I was a kid we subscribed to Consumer Reports and so I just assumed that everything that was good for the consumer--lower prices, better products, etc.--was a good thing. Upon reflection, though, I think it's a mistake to focus too narrowly on the interests of consumers. For example (from my Taleb review a couple years ago):

The discussion on page 112 of how Ralph Nader saved lives (mostly via seat belts in cars) reminds me of his car-bumper campaign in the 1970s. My dad subscribed to Consumer Reports then (he still does, actually, and I think reads it for pleasure--it must be one of those Depression-mentality things), and at one point they were pushing heavily for the 5-mph bumpers. Apparently there was some federal regulation about how strong car bumpers had to be, to withstand a crash of 2.5 miles per hour, or 5 miles per hour, or whatever--the standard had been 2.5 (I think), then got raised to 5, then lowered back to 2.5, and Consumer's Union calculated (reasonably correctly, no doubt) that the 5 mph standard would, in the net, save drivers money. I naively assumed that CU was right on this. But, looking at it now, I would strongly oppose the 5 mph standard. In fact, I'd support a law forbidding such sturdy bumpers. Why? Because, as a pedestrian and cyclist, I don't want drivers to have that sense of security. I'd rather they be scared of fender-benders and, as a consequence, stay away from me! Anyway, the point here is not to debate auto safety; it's just an interesting example of how my own views have changed. Another example of incentives.

Regarding airline deregulation, a lot of problems have been caused by cheap flights. And, even though I've personally benefited from the convenience, maybe overall we'd be better off with the old system of fewer, more expensive flights. Or maybe expansion was going to happen anyway, in which case it was probably a good idea to try to do things right.

Anyway . . . I never met Alfred Kahn but I heard a lot about him because he was my mother's adviser in college. She studied economics at Cornell and had only good things to say about Kahn, (She also took a course with Feller, the famed probabilist, but she didn't get so much out of that.) We were all very excited in 1978 or whenever it was when Kahn was in the news as the inflation czar. In a slightly different world, my mom would've been doing something like that, rather than staying at home with the kids and getting a mid-level job later in life.

P.S. I'm not claiming any expertise on airline deregulation! My point in bringing this up was to just indicate how my thinking (and that of others too, I'm sure) has changed since the 1970s. When the name of Alfred Kahn comes up, I'm immediately sent back in my mind to 1948 and 1978, so it's interesting to reflect upon intellectual and cultural changes since then.

From blogger Matthew Yglesias:

There are lots of great graphs all over the web (see, for example, here and here for some snappy pictures of unemployment trends from blogger "Geoff").

There's nothing special about Yglesias's graph. In fact, the reason I'm singling it out as "graph of the year" is because it's not special.

A couple people pointed me to this recent news article which discusses "why, beyond middle age, people get happier as they get older." Here's the story:

When people start out on adult life, they are, on average, pretty cheerful. Things go downhill from youth to middle age until they reach a nadir commonly known as the mid-life crisis. So far, so familiar. The surprising part happens after that. Although as people move towards old age they lose things they treasure--vitality, mental sharpness and looks--they also gain what people spend their lives pursuing: happiness.

This curious finding has emerged from a new branch of economics that seeks a more satisfactory measure than money of human well-being. Conventional economics uses money as a proxy for utility--the dismal way in which the discipline talks about happiness. But some economists, unconvinced that there is a direct relationship between money and well-being, have decided to go to the nub of the matter and measure happiness itself. . . There are already a lot of data on the subject collected by, for instance, America's General Social Survey, Eurobarometer and Gallup. . . .

And here's the killer graph:

All I can say is . . . it ain't so simple. I learned this the hard way. After reading a bunch of articles on the U-shaped relation between age and happiness--including some research that used the General Social Survey--I downloaded the GSS data (you can do it yourself!) and prepared some data for my introductory statistics class. I made a little dataset with happiness, age, sex, marital status, income, and a couple other variables and ran some regressions and made some simple graphs. The idea was to start with the fascinating U-shaped pattern and then discuss what could be learned further using some basic statistical techniques of subsetting and regression.

But I got stuck--really stuck. Here was my first graph, a quick summary of average happiness level (on a 0, 1, 2 scale; in total, 12% of respondents rated their happiness at 0 (the lowest level), 56% gave themselves a 1, and 32% described themselves as having the highest level on this three-point scale). And below are the raw averages of happiness vs. age. (Note: the graph has changed. In my original posted graph, I plotted the percentage of respondents of each age who had happiness levels of 1 or 2; this corrected graph plots average happiness levels.)

Uh-oh. I did this by single years of age so it's noisy--even when using decades of GSS, the sample's not infinite--but there's nothing like the famous U-shaped pattern! Sure, if you stare hard enough, you can see a U between ages 35 and 70, but the behavior from 20-35 and from 70-90 looks all wrong. There's a big difference between the publishedl graph, which has maxima at 20 and 85, and the my graph from GSS, which has minima at 20 and 85.

There are a lot of ways these graphs could be reconciled. There could be cohort or period effects, perhaps I should be controlling for other variables, maybe I'm using a bad question, or maybe I simply miscoded the data. All of these are possibilities. I spent several hours staring at the GSS codebook and playing with the data in different ways and couldn't recover the U. Sometimes I could get happiness to go up with age, but then it was just a gradual rise from age 18, without the dip around age 45 or 50. There's a lot going on here and I very well may still be missing something important. [Note: I imagine that sort of cagey disclaimer is typical of statisticians: by our training we are so aware of uncertainty. Researchers in other fields don't seem to feel the same need to do this.]

Anyway, at some point in this analysis I was getting frustrated at my inability to find the U (I felt like the characters in that old movie they used to show on TV on New Year's Eve, all looking for "the big W") and beginning to panic that this beautiful example was too fragile to survive in the classroom.

So I called Grazia Pittau, an economist (!) with whom I'd collaborated on some earlier happiness research (in which I contributed multilevel modeling and some ideas about graphs but not much of substance regarding psychology or economics). Grazia confirmed to me that the U-shaped pattern is indeed fragile, that you have to work hard to find it, and often it shows up when people fit linear and quadratic terms, in which case everything looks like a parabola. (I'd tried regressions with age & age-squared, but it took a lot of finagling to get the coefficient for age-squared to have the "correct" sign.)

And then I encountered a paper by Paul Frijters and Tony Beatton which directly addressed my confusion. Frijters and Beatton write:

Whilst the majority of psychologists have concluded there is not much of a relationship at all, the economic literature has unearthed a possible U-shape relationship. In this paper we [Frijters and Beatton] replicate the U-shape for the German SocioEconomic Panel (GSOEP), and we investigate several possible explanations for it.

They conclude that the U is fragile and that it arises from a sample-selection bias. I refer you to the above link for further discussion.

In summary: I agree that happiness and life satisfaction are worth studying--of course they're worth studying--but, in the midst of looking for explanations for that U-shaped pattern, it might be worth looking more carefully to see what exactly is happening. At the very least, the pattern does not seem to be as clear as implied from some media reports. (Even a glance at the paper by Stone, Schwartz, Broderick, and Deaton, which is the source of the top graph above, reveals a bunch of graphs, only some of which are U-shaped.) All those explanations have to be contingent on the pattern actually existing in the population.

My goal is not to debunk but to push toward some broader thinking. People are always trying to explain what's behind a stylized fact, which is fine, but sometimes they're explaining things that aren't really happening, just like those theoretical physicists who, shortly after the Fleischmann-Pons experiment, came up with ingenious models of cold fusion. These theorists were brilliant but they were doomed because they were modeling a phenomenon which (most likely) doesn't exist.

A comment from a few days ago by Eric Rasmusen seems relevant, connecting this to general issues of confirmation bias. If you make enough graphs and you're looking for a U, you'll find it. I'm not denying the U is there, I'm just questioning the centrality of the U to the larger story of age, happiness, and life satisfaction. There appear to be many different age patterns and it's not clear to me that the U should be considered the paradigm.

P.S. I think this research (even if occasionally done by economists) is psychology, not economics. No big deal--it's just a matter of terminology--but I think journalists and other outsiders can be misread if they hear about this sort of thing and start searching in the economics literature rather than in the psychology literature. In general, I think economists will have more to say than psychologists about prices, and psychologists will have more insights about emotions and happiness. I'm sure that economists can make important contributions to the study of happiness, just as psychologists can make important contributions to the study of prices, but even a magazine called "The Economist" should know the difference.

Catherine Bueker writes:

I [Bueker] am analyzing the effect of various contextual factors on the voter turnout of naturalized Latino citizens. I have included the natural log of the number of Spanish Language ads run in each state during the election cycle to predict voter turnout. I now want to calculate the predicted probabilities of turnout for those in states with 0 ads, 500 ads, 1000 ads, etc. The problem is that I do not know how to handle the beta coefficient of the LN(Spanish language ads). Is there someway to "unlog" the coefficient?

My reply: Calculate these probabilities for specific values of predictors, then graph the predictions of interest. Also, you can average over the other inputs in your model to get summaries. See this article with Pardoe for further discussion.

This link on education reform send me to this blog on foreign languages in Canadian public schools:

Interesting discussion by Alex Tabarrok (following up on an article by Rebecca Solnit) on the continuum between voluntarism (or, more generally, non-cash transactions) and markets with monetary exchange. I just have a few comments of my own:

1. Solnit writes of "the iceberg economy," which she characterizes as "based on gift economies, barter, mutual aid, and giving without hope of return . . . the relations between friends, between family members, the activities of volunteers or those who have chosen their vocation on principle rather than for profit." I just wonder whether "barter" completely fits in here. Maybe it depends on context. Sometimes barter is an informal way of keeping track (you help me and I help you), but in settings of low liquidity I could imagine barter being simply an inefficient way of performing an economic transaction.

2. I am no expert on capitalism but my impression is that it's not just about "competition and selfishness" but also is related to the ability of firms to build up and use capital. In that sense, I can see how it could be qualitatively different from barter. But I wonder whether Solnit is causing more confusion than clarity by lumping competition, selfishness, and capitalism into a single category. I'm reminded of my article with Edlin and Kaplan where we emphasize that "rational" != "selfish."

3. Tabarrok identifies capitalism with "markets," which again seems like only part of the picture. Sure, you can thing of Ebay, for example, as a more efficient version of neighbors sharing their unwanted objects, with all the advantages and all the disadvantages of "efficiency" (on one hand, you're more likely to get what you want and you can avoid interacting with annoying people; on the other hand, instead of interacting with people you're sitting at a computer--just as I am right now!). But there are a lot of other aspects of capitalism (from BP oil spills to plain old backstabbing corporate politics) that don't quite fit the "market" or "voluntary exchange" story. I'm not saying that capitalism is bad (or good), just that he seems to be talking more about trade than about capitalism in general.

A link from Tyler Cowen led me to this long blog article by Razib Khan, discussing some recent genetic findings on human origins in the context of the past twenty-five years of research and popularization of science.

Mark Palko comments on the (presumably) well-intentioned but silly Jumpstart test of financial literacy, which was given to 7000 high school seniors Given that, as we heard a few years back, most high school seniors can't locate Miami on a map of the U.S., you won't be surprised to hear that they flubbed item after item on this quiz.

But, as Palko points out, the concept is better than the execution:

I've become increasingly uncomfortable with the term "confidence interval," for several reasons:

- The well-known difficulties in interpretation (officially the confidence statement can be interpreted only on average, but people typically implicitly give the Bayesian interpretation to each case),

- The ambiguity between confidence intervals and predictive intervals. (See the footnote in BDA where we discuss the difference between "inference" and "prediction" in the classical framework.)

- The awkwardness of explaining that confidence intervals are big in noisy situations where you have less confidence, and confidence intervals are small when you have more confidence.

So here's my proposal. Let's use the term "uncertainty interval" instead. The uncertainty interval tells you how much uncertainty you have. That works pretty well, I think.

P.S. As of this writing, "confidence interval" outGoogles "uncertainty interval" by the huge margin of 9.5 million to 54000. So we have a ways to go.

Two positions open in the statistics group at the NYU education school. If you get the job, you get to work with Jennifer HIll!

One position is a postdoctoral fellowship, and the other is a visiting professorship. The latter position requires "the demonstrated ability to develop a nationally recognized research program," which seems like a lot to ask for a visiting professor. Do they expect the visiting prof to develop a nationally recognized research program and then leave it there at NYU after the visit is over?

In any case, Jennifer and her colleagues are doing excellent work, both applied and methodological, and this seems like a great opportunity.

Yesterday I wrote that Mickey Kaus was right to point out that it's time to retire Tip O'Neill's famous dictum that "all politics are local." As Kaus points out, all the congressional elections in recent decades have been nationalized. The slogan is particularly silly for Tip O'Neill himself. Sure, O'Neill had to have a firm grip on local politics to get his safe seat in the first place, but after that it was smooth sailing.

Jonathan Bernstein disagrees, writing:

From the Gallup Poll:

Four in 10 Americans, slightly fewer today than in years past, believe God created humans in their present form about 10,000 years ago.

They've been asking the question since 1982 and it's been pretty steady at 45%, so in some sense this is good news! (I'm saying this under the completely unsupported belief that it's better for people to believe truths than falsehoods.)

Word count stats from the Google books database prove that Bayesianism is expanding faster than the universe.

A n-gram is a tuple of n words.

Mickey Kaus does a public service by trashing Tip O'Neill's famous dictum that "all politics are local." As Kaus point out, all the congressional elections in recent decades have been nationalized.

I'd go one step further and say that, sure, all politics are local--if you're Tip O'Neill and represent a ironclad Democratic seat in Congress. It's easy to be smug about your political skills if you're in a safe seat and have enough pull in state politics to avoid your district getting gerrymandered. Then you can sit there and sagely attribute your success to your continuing mastery of local politics rather than to whatever it took to get the seat in the first place.

Nate writes:

The Yankees have offered Jeter $45 million over three years -- or $15 million per year. . . But that doesn't mean that the process won't be frustrating for Jeter, or that there won't be a few hurt feelings along the way. . . .

$45 million, huh? Even after taxes, that's a lot of money!

I have agreed to do a local art exhibition in February.

An excuse to think about form, colour and style for plotting almost individual observation likelihoods - while invoking the artists privilege of refusing to give interpretations of their own work.

In order to make it possibly less dry I'll try to use intuitive suggestive captions like in this example TheTyranyof13.pdf

thereby side stepping the technical discussions like here RadfordNealBlog

Suggested models and data sets (or even submissions) would be most appreciated.

I likely be sticking to realism i.e. plots that represent 'statistical reality' faithfully.

K?

Sciencedaily has posted an article titled Apes Unwilling to Gamble When Odds Are Uncertain:

The apes readily distinguished between the different probabilities of winning: they gambled a lot when there was a 100 percent chance, less when there was a 50 percent chance, and only rarely when there was no chance In some trials, however, the experimenter didn't remove a lid from the bowl, so the apes couldn't assess the likelihood of winning a banana The odds from the covered bowl were identical to those from the risky option: a 50 percent chance of getting the much sought-after banana. But apes of both species were less likely to choose this ambiguous option.

Like humans, they showed "ambiguity aversion" -- preferring to gamble more when they knew the odds than when they didn't. Given some of the other differences between chimps and bonobos, Hare and Rosati had expected to find the bonobos to be more averse to ambiguity, but that didn't turn out to be the case.

Thanks to Stan Salthe for the link.

Someone writes:

I am hoping you can give me some advice about when to use fixed and random effects model. I am currently working on a paper that examines the effect of . . . by comparing states . . .

It got reviewed . . . by three economists and all suggest that we run a fixed effects model. We ran a hierarchial model in the paper that allow the intercept and slope to vary before and after . . . My question is which is correct? We have ran it both ways and really it makes no difference which model you run, the results are very similar. But for my own learning, I would really like to understand which to use under what circumstances. Is the fact that we use the whole population reason enough to just run a fixed effect model?

Perhaps you can suggest a good reference to this question of when to run a fixed vs. random effects model.

I'm not always sure what is meant by a "fixed effects model"; see my paper on Anova for discussion of the problems with this terminology:
http://www.stat.columbia.edu/~gelman/research/published/AOS259.pdf
Sometimes there is a concern about fitting multilevel models when there are correlations; see this paper for discussion of how to deal with this:
http://www.stat.columbia.edu/~gelman/research/unpublished/Bafumi_Gelman_Midwest06.pdf

The short answer to your question is that, no, the fact that you use the whole population should not determine the model you fit. In particular, there is no reason for you to use a model with group-level variance equal to infinity. There is various literature with conflicting recommendations on the topic (see my Anova paper for references), but, as I discuss in that paper, a lot of these recommendations are less coherent than they might seem at first.

The title of this blog post quotes the second line of the abstract of Goldstein et al.'s much ballyhooed 2008 tech report, Do More Expensive Wines Taste Better? Evidence from a Large Sample of Blind Tastings.

The first sentence of the abstract is

Individuals who are unaware of the price do not derive more enjoyment from more expensive wine.

Perhaps not surprisingly, given the easy target wine snobs make, the popular press has picked up on the first sentence of the tech report. For example, the Freakonomics blog/radio entry of the same name quotes the first line, ignores the qualification, then concludes

Wishing you the happiest of holiday seasons, and urging you to spend $15 instead of $50 on your next bottle of wine. Go ahead, take the money you save and blow it on the lottery.

In case you're wondering about whether to buy me a cheap or expensive bottle of wine, keep in mind I've had classical "wine training". After ten minutes of training with some side by side examples, you too will be able to distinguish traditional old world wine from 3-buck Chuck in a double blind tasting. Whether you'll be able to tell a quality village Volnay from a premier cru's another matter.

There's another problem with the experimental design. Wines that stand out in a side-by-side tasting are not necessarily the ones you want to pair with food or even drink all night on their own.

The other problem is that some people genuinely prefer the 3 buck Chuck. Most Americans I've observed, including myself, start out enjoying sweeter new world style wines and then over time gravitate to more structured (tannic), complex (different flavors) and acidic wines.

Frank Hansen writes:

Columbus Park is on Chicago's west side, in the Austin neighborhood. The park is a big green area which includes a golf course.

Here is the google satellite view.

Here is the nytimes page. Go to Chicago, and zoom over to the census tract 2521, which is just north of the horizontal gray line (Eisenhower Expressway, aka I290) and just east of Oak Park. The park is labeled on the nytimes map.

The census data have around 50 dots (they say 50 people per dot) in the park which has no residential buildings.

Congressional district is Danny Davis, IL7. Here's a map of the district.

So, how do we explain the map showing ~50 dots worth of people living in the park. What's up with the algorithm to place the dots?

I dunno. I leave this one to you, the readers.

Giorgio Corani writes:

Your work on weakly informative priors is close to some research I [Corani] did (together with Prof. Zaffalon) in the last years using the so-called imprecise probabilities. The idea is to work with a set of priors (containing even very different priors); to update them via Bayes' rule and then compute a set of posteriors.

The set of priors is convex and the priors are Dirichlet (thus, conjugate to the likelihood); this allows to compute the set of posteriors exactly and efficiently.

I [Corani] have used this approach for classification, extending naive Bayes and TAN to imprecise probabilities. Classifiers based on imprecise probabilities return more classes when they find that the most probable class is prior-dependent, i.e., if picking different priors in the convex set leads to identify different classes as the most probable one. Instead of returning a single (unreliable) prior-dependent class, credal classifiers in this case preserve reliability by issuing a set-valued classification. By experiments, we have consistently found that Bayesian classifiers are unreliable on the instances which are classified in an indeterminate way (but reliably) by our classifiers.

This looks potentially interesting. It's not an approach I've ever thought about much (nor do I really have the time to think about it now, unfortunately), but I thought I'd post the link to some papers for those of you who might be interested. Imprecise priors have been proposed as a method for encoding weak prior information, so perhaps there is something important there.

My recent article with Xian and Judith. In English.

Interested readers can try to figure out which parts were written by each of the three authors (recognizing that each of us edited the whole thing).

Gur Huberman asks what I think of this magazine article by Johah Lehrer (see also here).

My reply is that it reminds me a bit of what I wrote here. Or see here for the quick powerpoint version: The short story is that if you screen for statistical significance when estimating small effects, you will necessarily overestimate the magnitudes of effects, sometimes by a huge amount. I know that Dave Krantz has thought about this issue for awhile; it came up when Francis Tuerlinckx and I wrote our paper on Type S errors, ten years ago.

My current thinking is that most (almost all?) research studies of the sort described by Lehrer should be accompanied by retrospective power analyses, or informative Bayesian inferences. Either of these approaches--whether classical or Bayesian, the key is that they incorporate real prior information, just as is done in a classical prospective power analysis--would, I think, moderate the tendency to overestimate the magnitude of effects.

In answer to the question posed by the title of Lehrer's article, my answer is Yes, there is something wrong with the scientific method, if this method is defined as running experiments and doing data analysis in a patternless way and then reporting, as true, results that pass a statistical significance threshold.

And corrections for multiple comparisons will not solve the problem: such adjustments merely shift the threshold without resolving the problem of overestimation of small effects.

i received the following press release from the Heritage Provider Network, "the largest limited Knox-Keene licensed managed care organization in California." I have no idea what this means, but I assume it's some sort of HMO.

In any case, this looks like it could be interesting:

Participants in the Health Prize challenge will be given a data set comprised of the de-identified medical records of 100,000 individuals who are members of HPN. The teams will then need to predict the hospitalization of a set percentage of those members who went to the hospital during the year following the start date, and do so with a defined accuracy rate. The winners will receive the $3 million prize. . . . the contest is designed to spur involvement by others involved in analytics, such as those involved in data mining and predictive modeling who may not currently be working in health care. "We believe that doing so will bring innovative thinking to health analytics and may allow us to solve at least part of the health care cost conundrum . . ."

I don't know enough about health policy to know if this makes sense. Ultimately, the goal is not to predict hospitalization, but to avoid it. But maybe if you can predict it well, it could be possible to design the system a bit better. The current system--in which the doctor's office is open about 40 hours a week, and otherwise you have to go the emergency room--is a joke.

Karri Seppa writes:

My topic is regional variation in the cause-specific survival of breast cancer patients across the 21 hospital districts in Finland, this component being modeled by random effects. I am interested mainly in the district-specific effects, and with a hierarchical model I can get reasonable estimates also for sparsely populated districts.

Based on the recommendation given in the book by yourself and Dr. Hill (2007) I tend to think that the finite-population variance would be an appropriate measure to summarize the overall variation across the 21 districts. However, I feel it is somewhat incoherent first to assume a Normal distribution for the district effects, involving a "superpopulation" variance parameter, and then to compute the finite-population variance from the estimated district-specific parameters. I wonder whether the finite-population variance were more appropriate in the context of a model with fixed district effects?

My reply:

Sander Wagner writes:

I just read the post on ethical concerns in medical trials. As there seems to be a lot more pressure on private researchers i thought it might be a nice little exercise to compare p-values from privately funded medical trials with those reported from publicly funded research, to see if confirmation pressure is higher in private research (i.e. p-values are closer to the cutoff levels for significance for the privately funded research). Do you think this is a decent idea or are you sceptical? Also are you aware of any sources listing a large number of representative medical studies and their type of funding?

My reply:

This sounds like something worth studying. I don't know where to get data about this sort of thing, but now that it's been blogged, maybe someone will follow up.

A couple months ago, the students in our Teaching Statistics class practiced one-on-one tutoring. We paired up the students (most of them are second-year Ph.D. students in our statistics department), with student A playing the role of instructor and student B playing the role of a confused student who was coming in for office hours. Within each pair, A tried to teach B (using pen and paper or the blackboard) for five minutes. Then they both took notes on what worked and what didn't work, and then they switched roles, so that B got some practice teaching.

While this was all happening, Val and I walked around the room and watched what they did. And we took some notes, and wrote down some ideas: In no particular order:

Who's holding the pen? Mort of the pairs did their communication on paper, and in most of these cases, the person holding the pen (and with the paper closest to him/herself) was the teacher. That ain't right. Let the student hold the pen. The student's the one who's gonna have to learn how to do the skill in question.

The split screen. One of the instructors was using the board in a clean and organized way, and this got me thinking of a new idea (not really new, but new to me) of using the blackboard as a split screen. Divide the board in half with a vertical line. 2 sticks of chalk: the instructor works on the left side of the board, the student on the right. On the top of each half of the split screen is a problem to work out. The two problems are similar but not identical. The instructor works out the solution on the left side while the student uses this as a template to solve the problem on the right.

Seeing one's flaws in others. It can be difficult to observe our own behavior. But sometimes when observing others, we can realize that we are doing the same thing ourselves. Thus I can learn from the struggles of our Ph.D. students and get ideas of how to be a better teacher myself (and then share these ideas with them and you).

Go with your strengths. One of our students (playing the role of instructor in the activity) speaks English with a strong accent (but much better than my accent when speaking French or Spanish, I'm sure). If your spoken language is hard to understand, adapt by talking less and writing more. You'll have plenty of chances to practice your speaking skills--outside of class.

Setting expectations. When a student comes in for office hours, he or she might have one question or two, or five. And this student might want to get out of your office as quickly as possible, or he or she might welcome the opportunity for a longer lesson. How you should behave will depend a lot on what the student wants. So ask the student: What are your expectations for this session? This needn't limit your interaction--it's perfectly fine for someone to come in with one question and then get involved in a longer exploration--but the student's initial expectations are a good place to start.

Any other thoughts?

If you have other ideas, please post them here. I've never been good at one-on-one teaching in introductory courses--I've always felt pretty useless sitting next to a student trying to make some point clear--but maybe with these new techniques, things will go better.

The American Statistical Association has an annual recommended gift list. (I think they had Red State, Blue State on the list a couple years ago.) They need some more suggestions in the next couple of days. Does anybody have any ideas?

It worked on this one.

Good maze designers know this trick and are careful to design multiple branches in each direction. Back when I was in junior high, I used to make huge mazes, and the basic idea was to anticipate what the solver might try to do and to make the maze difficult by postponing the point at which he would realize a path was going nowhere. For example, you might have 6 branches: one dead end, two pairs that form loops going back to the start, and one that is the correct solution. You do this from both directions and add some twists and turns, and there you are.

But the maze designer aiming for the naive solver--the sap who starts from the entrance and goes toward the exit--can simplify matters by just having 6 branches: five dead ends and one winner. This sort of thing is easy to solve in the reverse direction. I'm surprised the Times didn't do better for their special puzzle issue.

Tyler Cowen links to a "scary comparison" that claims that "a one-parent family of three making $14,500 a year (minimum wage) has more disposable income than a family making $60,000 a year."

Kaiser Fung looks into this comparison in more detail. As Kaiser puts it:

Dave Kane writes:

I [Kane] am involved in a dispute relating to whether or not a blog can be considered part of one's academic writing. Williams College restricts the use of undergraduate theses as follows:

Non-commercial, academic use within the scope of "Fair Use" standards is acceptable. Otherwise, you may not copy or distribute any content without the permission of the copyright holder.

Seems obvious enough. Yet some folks think that my use of thesis material in a blog post fails this test because it is not "academic." See this post for the gory details.

As we said in Red State, Blue State, it's not the Prius vs. the pickup truck, it's the Prius vs. the Hummer. Here's the graph:

Or, as Ross Douthat put it in an op-ed yesterday:

This means that a culture war that's often seen as a clash between liberal elites and a conservative middle America looks more and more like a conflict within the educated class -- pitting Wheaton and Baylor against Brown and Bard, Redeemer Presbyterian Church against the 92nd Street Y, C. S. Lewis devotees against the Philip Pullman fan club.

Our main motivation for doing this work was to change how the news media think about America's political divisions, and so it's good to see our ideas getting mainstreamed and moving toward conventional wisdom.

Aleks points me to this research summary from Dan Goldstein. Good stuff. I've heard of a lot of this--I actually use some of it in my intro statistics course, when we show the students how they can express probability trees using frequencies--but it's good to see it all in one place.

From Discover:

Razib Khan asks:

But follow the gradient from El Paso to the Illinois-Missouri border. The differences are small across state lines, but the consistent differences along the borders really don't make. Are there state-level policies or regulations causing this? Or, are there state-level differences in measurement? This weird pattern shows up in other CDC data I've seen.

Turns out that CDC isn't providing data, they're providing model. Frank Howland answered:

I suspect the answer has to do with the manner in which the county estimates are produced. I went to the original data source, the CDC, and then to the relevant FAQ.

There they say that the diabetes prevalence estimates come from the "CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the U.S. Census Bureau's Population Estimates Program. The BRFSS is an ongoing, monthly, state-based telephone survey of the adult population. The survey provides state-specific information"

So the CDC then uses a complicated statistical procedure ("indirect model-dependent estimates" using Bayesian techniques and multilevel Poisson regression models) to go from state to county prevalence estimates. My hunch is that the state level averages thereby affect the county estimates. The FAQ in fact says "State is included as a county-level covariate."

I'd prefer to have real data, not a model. I'd do the model myself, thank you. Data itself is tricky enough, as J. Stamp said.

OK, here's something that is completely baffling me. I read this article by John Colapinto on the neuroscientist V. S. Ramachandran, who's famous for his innovative treatment for "phantom limb" pain:

His first subject was a young man who a decade earlier had crashed his motorcycle and torn from his spinal column the nerves supplying the left arm. After keeping the useless arm in a sling for a year, the man had the arm amputated above the elbow. Ever since, he had felt unremitting cramping in the phantom limb, as though it were immobilized in an awkward position. . . . Ramachandram positioned a twenty-inch-by-twenty-inch drugstore mirror . . . and told him to place his intact right arm on one side of the mirror and his stump on the other. He told the man to arrange the mirror so that the reflection created the illusion that his intact arm was the continuation of the amputated one. The Ramachandran asked the man to move his right and left arms . . . "Oh, my God!" the man began to shout. . . . For the first time in ten years, the patient could feel his phantom limb "moving," and the cramping pain was instantly relieved. After the man had used the mirror therapy ten minutes a day for a month, his phantom limb shrank . . .

Ramachandran conducted the experiment on eight other amputees and published the results in Nature, in 1995. In all but one patient, phantom hands that had been balled into painful fists opened, and phantom arms that had stiffened into agonizing contortions straightened. . . .

So far, so good. But then the story continues:

Dr. Jack Tsao, a neurologist for the U.S. Navy . . . read Ramachandran's Nature paper on mirror therapy for phantom-limb pain. . . . Several years later, in 2004, Tsao began working at Walter Reed Military Hospital, where he saw hundreds of soldiers with amputations returning from Iraq and Afghanistan. Ninety percent of them had phantom-limb pain, and Tsao, noting that the painkillers routinely prescribed for the condition were ineffective, suggested mirror therapy. "We had a lot of skepticism from the people at the hospital, my colleagues as well as the amputee subjects themselves," Tsao said. But in a clinical trial of eighteen service members with lower-limb amputations . . . the six who used the mirror reported that their pain decreased [with no corresponding improvement in the control groups] . . . Tsao published his results in the New England Journal of Medicine, in 2007. "The people who really got completely pain-free remain so, two years later," said Tsao, who is currently conducting a study involving mirror therapy on upper-limb amputees at Walter Reed.

At first, this sounded perfectly reasonable: Bold new treatment is dismissed by skeptics but then is proved to be a winner in a clinical trial. But . . . wait a minute! I have some questions:

1. Ramachandran published his definitive paper in 1995 in a widely-circulated journal. Why did his mirror therapy not become the standard approach, especially given that "the painkillers routinely prescribed for the condition were ineffective"? Why were these ineffective painkillers "routinely prescribed" at all?

2. When Tsao finally got around to trying a therapy that had been published nine years before why did they have "a lot of skepticism from the people at the hospital"?

3. If Tsao saw "hundreds of soldiers" with phantom-limb pain, why did he try the already-published mirror therapy on only 18 of them?

4. How come, in 2009, two years after his paper in the New England Journal of Medicine--and fourteen years after Ramachandran's original paper in Nature--even now, Tsao is "currently conducting a study involving mirror therapy"? Why isn't he doing mirror therapy on everybody?

Ok, maybe I have the answer to the last question: Maybe Tsao's current (as of 2009) study is of different variants of mirror therapy. That is, maybe he is doing it on everybody, just in different ways. That would make sense.

But I don't understand items 1,2,3 above at all. There must be some part of the story that I'm missing. Perhaps someone could explain?

P.S. More here.

This conference touches nicely on many of the more Biostatistics related topics that have come up on this blog from a pragmatic and perceptive Bayesian perspective.

Fourth Annual Bayesian Biostatistics Conference

Including the star of that recent Cochrane TV debate who will be the key note speaker.

See here Subtle statistical issues to be debated on TV. and perhaps the last comment which is my personal take on that debate.

Reruns are still available here http://justin.tv/cochranetv/b/272278382

K?

Upon returning from sabbatical I came across a few magazines from a year ago that I hadn't gotten around to reading. I'm thinking that I should read everything on a one-year delay. The too-topical stuff (for example, promos tied to upcoming movies) I can ignore, and other items are enhanced by knowing what happened a year or two later.

For example, the 11 May 2009 issue of the New Yorker featured an article by Douglas McGray about an organization in Los Angeles called Green Dot that runs charter schools. According to the article, Green Dot, unlike typical charter school operators, educate just about everyone in their schools' areas and so don't benefit so much from selection. I don't know enough about the details to evaluate these claims, but I was curious about this bit:

[L.A. schools superintendent] Cortines has also agreed in principle to a partnership in Los Angeles. . . . Green Dot could take over as many as five Los Angeles schools in 2010, and maybe more. This month, Barr expects to meet again with [teacher's union leader] Weingarten and her staff and outline plans for a Green Dot America . . . Their first city would most likely be Washington, D.C. "If we're successful there, we'll get the attention of a lot of lawmakers," Barr said. . . There are risks for Barr [the operator of Green Dot] in this kind of expansion. It will be months, and maybe years, before there's hard evidence about what Green Dot has accomplished at Locke [High School]. And that one takeover put a real strain on the organization. . . .

A year and a half have passed. What's happened, I wonder?

Fabio Rojas writes:

Hal Varian sends in this link to a series of educational videos described to be "a journey into the heart of statistics." It seems to be focused on exploratory data analysis, which it describes as "an extraordinary new method of understanding ourselves and our Universe."

Xuequn Hu writes:

I am an econ doctoral student, trying to do some empirical work using Bayesian methods. Recently I read a paper(and its discussion) that pitches Bayesian methods against GMM (Generalized Method of Moments), which is quite popular in econometrics for frequentists. I am wondering if you can, here or on your blog, give some insights about these two methods, from the perspective of a Bayesian statistician. I know GMM does not conform to likelihood principle, but Bayesian are often charged with strong distribution assumptions.

I can't actually help on this, since I don't know what GMM is. My guess is that, like other methods that don't explicitly use prior estimation, this method will work well if sufficient information is included as data. Which would imply a hierarchical structure.

Posted at MediaBistro:

The Harvard Sports Analysis Collective are the group that tackles problems such as “Who wrote this column: Bill Simmons, Rick Reilly, or Kevin Whitlock?” and “Should a football team give up free touchdowns?”

It’s all fun and games, until the students land jobs with major teams.

According to the Harvard Crimson, sophomore John Ezekowitz and junior Jason Rosenfeld scored gigs with the Phoenix Suns and the Shanghai Sharks, respectively, in part based on their work for HSAC.

It’s perhaps not a huge surprise that the Sharks would be interested in taking advantage of every available statistic. They are owned by Yao Ming, who plays for the Houston Rockets. The Rockets, in turn, employ general manager Daryl Morey who Simmons nicknamed “Dork Elvis” for his ahead of the curve analysis. (See Michael Lewis‘ The No Stats All-Star for an example.) But still, it’s very cool to see the pair get an opportunity to change the game.

Seth sent along an article (not by him) from the psychology literature and wrote:

This is a good example of your complaint about statistical significance. The authors want to say that predictability of information determines how distracting something is and have two conditions that vary in predictability. One is significantly distracting, the other isn't. But the two conditions are not significantly different from each other. So the two conditions are different more weakly than p = 0.05.

I don't think the reviewers failed to notice this. They just thought it should be published anyway, is my guess.

To me, the interesting question is: where should the bar be? at p = 0.05? at p = 0.10? something else? How can we figure out where to put the bar?

I replied:

My quick answer is that we have to get away from .05 and .10 and move to something that takes into account prior information. This could be Bayesian (of course) or could be done classically using power calculations, as discussed in this article.

In the annals of hack literature, it is sometimes said that if you aim to write best-selling crap, all you'll end up with is crap. To truly produce best-selling crap, you have to have a conviction, perhaps misplaced, that your writing has integrity. Whether or not this is a good generalization about writing, I have seen an analogous phenomenon in statistics: If you try to do nothing but model the data, you can be in for a wild and unpleasant ride: real data always seem to have one more twist beyond our ability to model (von Neumann's elephant's trunk notwithstanding). But if you model the underlying process, sometimes your model can fit surprisingly well as well as inviting openings for future research progress.

Posted by Phil Price:

A blogger (can't find his name anywhere on his blog) points to an article in the medical literature in 1994 that is...well, it's shocking, is what it is. This is from the abstract:

In Tai's Model, the total area under a curve is computed by dividing the area under the curve between two designated values on the X-axis (abscissas) into small segments (rectangles and triangles) whose areas can be accurately calculated from their respective geometrical formulas. The total sum of these individual areas thus represents the total area under the curve. Validity of the model is established by comparing total areas obtained from this model to these same areas obtained from graphic method (less than +/- 0.4%). Other formulas widely applied by researchers under- or overestimated total area under a metabolic curve by a great margin

The blogger finds this amusing, but I find it mostly upsetting and sad. Which is sadder: (1) That this paper got past the referees, (2) that it has been cited dozens of times in the medical literature, including this year, (3) that, if the abstract is to be believed, many medical researchers DON'T use an accurate method to calculate the area under a curve.

Things gets reinvented all the time. I, too, have published results that I've later found were previously published by someone else. But I've never done it with something that is taught in high school calculus. And --- I'm practically spluttering with indignation --- if I wanted to calculate something like the area under a curve, I would at least first see if there is already a known way to do it! I wouldn't invent an obvious method, name it after myself, and send it to a journal, without it ever occurring to me that, gee, maybe someone else has thought about this already! Grrrrrr.

Ole Rogeberg sends in this:

and writes:

No idea if this is amusing to non-economists, but I tried my hand at the xtranormal-trend. It's an attempt to spoof the many standard "incantations" I've encountered over the years from economists who don't want to agree that rational addiction theory lacks justification for some of the claims it makes. More specifically, the claims that the theory can be used to conduct welfare analysis of alternative policies.

See here (scroll to Rational Addiction) and here for background.

After seeing this graph:

I have the following message for Sharad:

Rotate the graph 90 degrees so you can see the words. Also you can ditch the lines. Then what you have is a dotplot, following the principles of Cleveland (1985). You can lay out a few on one page to see some interactions with demographics.

The real challenge here . . .

. . . is to automate this sort of advice. Or maybe we just need a really nice dotplot() function and enough examples, and people will start doing it?

P.S.

Often a lineplot is better. See here for a discussion of another Sharad example.

I can't use this on my PC, but the link comes from Aleks, so maybe it's something good!

Is there an implementation of bayesglm in Stata? (That is, approximate maximum penalized likelihood estimation with specified normal or t prior distributions on the coefficients.)

Daniel Drezner takes on Bill James.

I just gave my first Skype presentation today, and it felt pretty strange.

The technical difficulties mostly arose with the sound. There were heavy echoes and so we ended up just cutting off the sound from the audience. This made it more difficult for me because I couldn't gauge audience reaction. It was a real challenge to give a talk without being able to hear the laughter of the audience. (I asked them to wave their hands every time they laughed, but they didn't do so--or else they were never laughing, which would be even worse.)

Next time I'll use the telephone for at least one of the sound channels.

The visuals were ok from my side--I just went thru my slides one by one, using the cursor to point to things. I prefer standing next to the screen and pointing with my hands. But doing it this way was ok, considering.

The real visual problem went the other way: I couldn't really see the audience. From the perspective of the little computer camera, everyone seemed far away and I couldn't really sense their reactions. I wonder if next time it would be better to focus on just one or two people in the audience whom I could see clearly.

My overall feeling was that it was strange to give a talk in an isolation booth with no feedback. Also, the talk itself was a bit unusual for me in that very little of it was about my own research. It's my own ideas (joint with Antony Unwin) but almost all the graphs are by others.

A few days after "Dramatic study shows participants are affected by psychological phenomena from the future," (see here) the British Psychological Society follows up with "Can psychology help combat pseudoscience?."

Somehow I'm reminded of that bit of financial advice which says, if you want to save some money, your best investment is to pay off your credit card bills.

See below. W. D. Burnham is a former professor of mine, T. Ferguson does important work on money and politics, and J. Stiglitz is a colleague at Columbia (whom I've never actually met). Could be interesting.

I guess there's a reason they put this stuff in the Opinion section and not in the Science section, huh?

P.S. More here.

For awhile I've been curious (see also here) about the U-shaped relation between happiness and age (with people least happy, on average, in their forties, and happier before and after).

But when I tried to demonstrate it to me intro statistics course, using the General Social Survey, I couldn't find the famed U, or anything like it. Using pooled GSS data mixes age, period, and cohort, so I tried throwing in some cohort effects (indicators for decades) and a couple other variables, but still couldn't find that U.

So I was intrigued when I came across this paper by Paul Frijters and Tony Beatton, who write:

Whilst the majority of psychologists have concluded there is not much of a relationship at all, the economic literature has unearthed a possible U-shape relationship. In this paper we [Frijters and Beatton] replicate the U-shape for the German SocioEconomic Panel (GSOEP), and we investigate several possible explanations for it.

They write:

What is the relationship between happiness and age? Do we get more miserable as we get older, or are we perhaps more or less equally happy throughout our lives with only the occasional special event (marriage, birth, promotion, health shock) that temporarily raises or reduces our happiness, or do we actually get happier as life gets on and we learn to be content with what we have?

The answer to this question in the recent economic literature on the subject is that the age-happiness relationship is U-shaped. This finding holds for the US, Germany, Britain, Australia, Europe, and apparently even South Africa. The stylised finding is that individuals gradually get unhappier after their 18th birthday, with a dip around 50 followed by a gradual upturn in old age. The predicted effect of age can be quite large, i.e. the difference in average happiness between an 18 year old and a 50 year old can be as much as 1.5 points on a 10 point scale.

Their conclusion:

The inclusion of the usual socio-economic variables in a cross-section leads to a U-shape in age that results from indirectly-age-related reverse causality. Putting it simply: good things, like getting a job and getting married, appear to happen to middle aged individuals who were already happy. . . . The found effect of age in fixed-effect regressions is simply too large and too out of line with everything else we know to be believable. The difference between first-time respondents and stayers and between the number of years someone stays in the panel doesn't allow for explanations based on fixed traits or observables. There has to be either a problem on the left-hand side (i.e. the measurement of happiness over the life of a panel) or on the right-hand side (selection on time-varying unobservables).

They think it's a sample-selection bias and not a true U-shaped pattern. Another stylized fact bites the dust (perhaps).

My column in Scientific American.

Check out the comments. I have to remember never ever to write about guns.

Steve Hsu, who started off this discussion, had some comments on my speculations on the personality of John von Neumann and others. Steve writes:

I [Hsu] actually knew Feynman a bit when I was an undergrad, and found him to be very nice to students. Since then I have heard quite a few stories from people in theoretical physics which emphasize his nastier side, and I think in the end he was quite a complicated person like everyone else.

There are a couple of pseudo-biographies of vN, but none as high quality as, e.g., Gleick's book on Feynman or Hodges book about Turing. (Gleick studied physics as an undergrad at Harvard, and Hodges is a PhD in mathematical physics -- pretty rare backgrounds for biographers!) For example, as mentioned on the comment thread to your post, Steve Heims wrote a book about both vN and Wiener (!), and Norman Macrae wrote a biography of vN. Both books are worth reading, but I think neither really do him justice. The breadth of vN's work is just too much for any one person to absorb, ranging from pure math to foundations of QM, to shock wave theory (important for nuclear weapons), to game theory, to computation.

I read the biography of Gell-Mann that came out several years ago, and it made me feel sad for the guy. In particular, I'm thinking about the bit where, after Feynman hit the bestseller list, Gell-Mann got a big book contract himself, but then he got completely blocked and couldn't figure out what to put in the book (which eventually became the unreadable but respectfully-reviewed The Quark and the Jaguar).

I'm still interested in the von Neumann paradox, but given what's been written in the comment thread so far, I'm at this point doubting that it will ever be resolved to my satisfaction. If only I could bring Ulam back to life and ask him a few questions, I'm sure he could explain. Ulam definitely seems like my kind of guy.

Medical researchers care about main effects, psychologists care about interactions. In psychology, the main effects are typically obvious, and it's only the interactions that are worth studying.

. . . they're not in awe of economists.

In contrast, economists sometimes treat each other with the soft bigotry of low expectations. For example, here's Brad DeLong in defense of Larry Summers:

[During a 2005 meeting, Summers] said that in a modern economy with sophisticated financial markets we were likely to have more and bigger financial crises than we had before, just as the worst modern transportation accidents are worse than the worst transportation accidents back in horse-and-buggy days. . . . Indeed, for twenty years one of Larry's conversation openers has been: "You really should write something else good on positive-feedback trading and its dangers for financial markets."

That's fine, but, hey, I've been going around saying this for many years too, and I'm not even an economist (although I did get an A in the last econ class I took, which was in eleventh grade). Lots and lots of people have been talking for years about the dangers of positive feedback, the risks of insurers covering the small risks and thus increasing correlation in the system and setting up big risks, etc.

I don't think Summers, as one of the world's foremost economists, deserves much credit for noticing this theoretical problem too and going around telling people that they "really should write something" on the topic. You get credit by doing, not by telling other people to do.

I think Steve Hsu (see above link) gets the point. No one's going to go around saying that some physicist is a genius because he's been going around for twenty years with a conversation opener like, "Hey--general relativity and quantum mechanics are incoherent. You should really write something about how to put them together in a single mathematical model."

P.S. Just to be clear, I'm not trying to argue with DeLong on the economics here. He may be completely right that Rajan was wrong and Summers was right in their 2005 exchange. But I do think he's a bit too overawed by Summers's putative brilliance. In a dark room with many of the lights covered up by opaque dollar bills, even a weak and intermittent beam can appear brilliant, if you look right at it.

I, like Steve Hsu, I too would love to read a definitive biography of John von Neumann (or, as we'd say in the U.S., "John Neumann"). I've read little things about him in various places such as Stanislaw Ulam's classic autobiography, and two things I've repeatedly noticed are:

1. Neumann comes off as a obnoxious, self-satisfied jerk. He just seems like the kind of guy I wouldn't like in real life.

2. All these great men seem to really have loved the guy.

It's hard for me to reconcile two impressions above. Of course, lots of people have a good side and a bad side, but what's striking here is that my impressions of Neumann's bad side come from the very stories that his friends use to demonstrate how lovable he was! So, yes, I'd like to see the biography--but only if it could resolve this paradox.

Also, I don't know how relevant this is, but Neumann shares one thing with the more-lovable Ulam and the less-lovable Mandelbrot: all had Jewish backgrounds but didn't seem to like to talk about it.

P.S. Just to calibrate, here are my impressions of some other famous twentieth-century physicists. In all cases this is based on my shallow reading, not from any firsthand or even secondhand contact:

Feynman: Another guy who seemed pretty unlikable. Phil and I use the term "Feynman story" for any anecdote that someone tells that is structured so that the teller comes off as a genius and everyone else in the story comes off as an idiot. Again, lots of people, from Ulam to Freeman Dyson on down, seemed to think Feynman was a great guy. But I think it's pretty clear that a lot of other people didn't think he was so great. So Feynman seems like a standard case of a guy who was nice to some people and a jerk to others.

Einstein: Everyone seems to describe him as pretty remote, perhaps outside the whole "nice guy / jerk" spectrum entirely.

Gell-Mann (or, as we'd say in the U.S., "Gelman"): Nobody seemed to like him so much. He doesn't actually come off as a bad guy in any way, just someone who, for whatever reason, isn't so lovable.

Fermi, Bohr, Bethe: In contrast, everyone seemed to love these guys.

Hawking: What can you say about a guy with this kind of disability?

Oppenheimer: A tragic figure etc etc. I don't think anyone called him likable.

Teller: Even less likable, apparently.

That's about it. (Sorry, I'm not very well-read when it comes to physics gossip. I don't know, for example, if any Nobel-Prize-winning physicists have tried to run down any of their colleagues in a parking lot.)

Paul Erdos is another one: He always seems to be described as charmingly eccentric, but from all the descriptions I've read, he sounds just horrible! Perhaps the key is to come into these interactions with appropriate expectations, then everything will be OK.

Maybe Michael Frayn would have some insight into this . . . not that I have any way of reaching him!

Aleks points me to this attractive visualization by David Sparks of U.S. voting.

On the plus side, the pictures and associated movie (showing an oddly horizontally-stretched-out United States) are pretty and seem to have gotten a bit of attention--the maps have received 31 comments, which is more than we get on almost all our blog entries here.

On the minus side, the movie is misleading. In many years it shows the whole U.S. as a single color, even when candidates from both parties won some votes. The text has errors too, for example the false claim that the South favored a Democratic candidate in 1980. The southern states that Jimmy Carter carried in 1980 were Georgia and . . . that's it.

But, as Aleks says, once this tool is out there, maybe people can use it to do better. It's in that spirit that I'm linking. Ya gotta start somewhere.

Also, this is a good example of a general principle: When you make a graph, look at it carefully to see if it makes sense!

Indeed.

Scott Berry, Brad Carlin, Jack Lee, and Peter Muller recently came out with a book with the above title.

The book packs a lot into its 280 pages and is fun to read as well (even if they do use the word "modalities" in their first paragraph, and later on they use the phrase "DIC criterion," which upsets my tidy, logical mind). The book starts off fast on page 1 and never lets go.

Clinical trials are a big part of statistics and it's cool to see the topic taken seriously and being treated rigorously. (Here I'm not talking about empty mathematical rigor (or, should I say, "rigor"), so-called optimal designs and all that, but rather the rigor of applied statistics, mapping models to reality.)

Also I have a few technical suggestions.

1. The authors fit a lot of models in Bugs, which is fine, but they go overboard on the WinBUGS thing. There's WinBUGS, OpenBUGS, JAGS: they're all Bugs recommend running Bugs from R using the clunky BRugs interface rather than the smoother bugs() function, which has good defaults and conveniently returns graphical summaries and convergence diagnostics. The result is to get tangled in software complications and distance the user from statistical modeling.

2. On page 61 they demonstrate an excellent graphical summary that reveals that, in a particular example, their posterior distribution is improper--or, strictly speaking, that the posterior depends strongly on the choice of an arbitrary truncation point in the prior distribution. But then they stick with the bad model! Huh? This doesn't seem like such a good idea.

3. They cover all of Bayesian inference in a couple chapters, which is fine--interested readers can learn the whole thing from the Carlin and Louis book--but in their haste they sometimes slip up. For example, from page 5:

Randomization minimizes the possibility of selection bias, and it tends to balance the treatment groups over covariates, both known and unknown. There are difference, however, in the Bayesian and frequentist views of randomization. In the latter, randomization serves as the basis for inference, whereas the basis for inference in the Bayesian approach is subjective probability, which does not require randomization.

I get their general drift but I don't agree completely. First, randomization is a basis for frequentist inference, but it's not fair to call it the basis. There's lots of frequentist inference for nonrandomized studies. Second, I agree that the basis for Bayesian inference is probability but I don't buy the "subjective" part (except to the extent that all science is subjective). Third, the above paragraph leaves out why a Bayesian would want to randomize. The basic reason is robustness, as we discuss in chapter 7 of BDA.

4. I was wondering what the authors would say about Sander Greenland's work on multiple-bias modeling. Greenland uses Bayesian methods and has thought a lot about bias and causal inference in practical medical settings. I looked up Greenland in the index and all I could find was one page, which referred to some of his more theoretical work:

Greenland, Lanes, and Jara (2008) explore the use of structural nested models and advocate what they call g-estimation, a form of test-based estimation adhering to the ITT principle and accomodating a semiparametric Cox partial likelihood.

Nothing on multiple-bias modeling. Also I didn't see any mention of this paper by John "no relation" Carlin and others. Finally, the above paragraph is a bit odd in that "test-based estimation" and "semiparametric Cox partial likelihood" are nowhere defined in the book (or, at least, I couldn't find them in the index). I mean, sure, the reader can google these things, but I'd really like to see these ideas presented in the context of the book.

5. The very last section covers subgroup analysis and then mentions multilevel models (the natural Bayesian approach to the problem) but then doesn't really follow through. They go into a long digression on decision analysis. That's fine, but I'd like to see a worked example of a multilevel model for subgroup analysis, instead of just the reference to Hodges et al. (2007).

In summary, I like this book and it left me wanting even more. I hope that everyone working on clinical trials reads it and that it has a large influence.

And, just to be clear, most of my criticisms above are of the form, "I like it and want more." In particular, my own books don't have anything to say on multiple-bias models, test-based estimation, semiparametric Cox partial likelihood, multilevel models for subgroup analysis, or various other topics I'm asking for elaboration on. As it stands, Berry, Carlin, Lee, and Muller have packed a lot into 280 pages.

The deadline for this year's Earth Institute postdocs is 1 Dec, so it's time to apply right away! It's a highly competitive interdisciplinary program, and we've had some statisticians in the past.

We're particularly interested in statisticians who have research interests in development and public health. It's fine--not just fine, but ideal--if you are interested in statistical methods also.

I came across an interesting article by T. W. Farnam, "Political divide between coasts and Midwest deepening, midterm election analysis shows."

There was one thing that bugged me, though.

Near the end of the article, Farnam writes:

Latinos are not swing voters . . . Exit polls showed that 60 percent of Latino voters favored Democratic House candidates - a relatively steady proportion with the 69 percent the party took in 2006, the year it captured 31 seats.

Huh? In what sense is 60% close to 69%? That's a swing of 9 percentage points. The national swing to the Republicans can be defined in different ways (depending on how you count uncontested races, and whether you go with total vote or average district vote) but in any case was something like 8 percentage points.

The swing among Latinos was, thus, about the same as the national swing. At least based on these data, the statement "Latinos are not swing voters" does not seem supported by the facts. Unless you also want to say that whites are not swing voters either.

Hal Varian writes:

You might find this a useful tool for cleaning data.

I haven't tried it out yet, but data cleaning is a hugely important topic and so this could be a big deal.

It probably got caught in the spam filter. We get tons and tons of spam (including the annoying spam that I have to remove by hand).

If your comment was accompanied by an ad or a spam link, then maybe I just deleted it.

John Haubrick writes:

Next semester I want to center my statistics class around independent projects that they will present at the end of the semester. My question is, by centering around a project and teaching for the different parts that they need at the time, should topics such as hypothesis testing be moved toward the beginning of the course? Or should I only discuss setting up a research hypothesis and discuss the actual testing later after they have the data?

My reply:

I'm not sure. There always is a difficulty of what can be covered in a project. My quick thought is that a project will perhaps work better if it is focused on data collection or exploratory data analysis rather than on estimation and hypothesis testing, which are topics that get covered pretty well in the course as a whole.

Ossama Hamed writes in with a new dynamic graphing software:

Reading somebody else's statistics rant made me realize the inherent contradictions in much of my own statistical advice.

John Salvatier pointed me to this blog on derivative based MCMC algorithms (also sometimes called "hybrid" or "Hamiltonian" Monte Carlo) and automatic differentiation as the future of MCMC.

This all makes sense to me and is consistent both with my mathematical intuition from studying Metropolis algorithms and my experience with Matt using hybrid MCMC when fitting hierarchical spline models. In particular, I agree with Salvatier's point about the potential for computation of analytic derivatives of the log-density function. As long as we're mostly snapping together our models using analytically-simple pieces, the same part of the program that handles the computation of log-posterior densities should also be able to compute derivatives analytically.

I've been a big fan of automatic derivative-based MCMC methods since I started hearing about them a couple years ago (I'm thinking of the DREAM project and of Mark Girolami's paper), and I too wonder why they haven't been used more. I guess we can try implementing them in our current project in which we're trying to fit models with deep interactions. I also suspect there are some underlying connections between derivative-based jumping rules and redundant parameterizations for hierarchical models.

It's funny. Salvatier is saying what I've been saying (not very convincingly) for a couple years. But, somehow, seeing it in somebody else's words makes it much more persuasive, and again I'm all excited about this stuff.

My only amendment to Salvatier's blog is that I wouldn't refer to these as "new" algorithms; they've been around for something like 25 years, I think.

My econ dept colleague Joseph Stiglitz suggests that financial fraudsters be sent to prison. He points out that the usual penalty--million-dollar fines--just isn't enough for crimes whose rewards can be in the hundreds of millions of dollars.

That all makes sense, but why do the options have to be:

1. No punishment

2. A fine with little punishment or deterrent value

3. Prison.

What's the point of putting nonviolent criminals in prison? As I've said before, I'd prefer if the government just took all these convicted thieves' assets along with 95% of their salary for several years, made them do community service (sorting bottles and cans at the local dump, perhaps; a financier should be good at this sort of thing, no?), etc. If restriction of personal freedom is judged be part of the sentence, they could be given some sort of electronic tag that would send a message to the police if you are ever more than 3 miles from your home. And a curfew so you have to stay home between the hours of 7pm and 7am. Also take away internet access and require that you live in a 200-square-foot apartment in a grungy neighborhood. And so forth. But no need to bill the taxpayers for the cost of prison.

Stiglitz writes:

When you say the Pledge of Allegiance you say, with "justice for all." People aren't sure that we have justice for all. Somebody is caught for a minor drug offense, they are sent to prison for a very long time. And yet, these so-called white-collar crimes, which are not victimless, almost none of these guys, almost none of them, go to prison.

To me, though, this misses the point. Why send minor drug offenders to prison for a very long time? Instead, why not just equip them with some sort of recorder/transmitter that has to be always on. If they can do all their drug deals in silence, then, really, how much trouble are they going to be causing?

Readers with more background in criminology than I will be able to poke holes in my proposals, I'm sure.

P.S. to the impatient readers out there: Yeah, yeah, I have some statistics items on deck. They'll appear at the approximate rate of one a day.

Can somebody please fix the pdf reader so that it can correctly render "ff" when I cut and paste? This comes up when I'm copying sections of articles on to the blog.

Thank you.

P.S. I googled "ff pdf" but no help there.

P.P.S. It's a problem with "fi" also.

P.P.P.S. Yes, I know about ligatures. But, if you already knew about ligatures, and I already know about ligatures, then presumably the pdf people already know about ligatures too. So why can't their clever program, which can already find individual f's, also find the ff's and separate them? I assume it's not so simple but I don't quite understand why not.

Raymond Lim writes:

Do you have any recommendations on clustering and binary models? My particular problem is I'm running a firm fixed effect logit and want to cluster by industry-year (every combination of industry-year). My control variable of interest in measured by industry-year and when I cluster by industry-year, the standard errors are 300x larger than when I don't cluster. Strangely, this problem only occurs when doing logit and not OLS (linear probability). Also, clustering just by field doesn't blow up the errors. My hunch is it has something to do with the non-nested structure of year, but I don't understand why this is only problematic under logit and not OLS.

My reply:

I'd recommend including four multilevel variance parameters, one for firm, one for industry, one for year, and one for industry-year. (In lmer, that's (1 | firm) + (1 | industry) + (1 | year) + (1 | industry.year)). No need to include (1 | firm.year) since in your data this is the error term. Try some varying slopes too.

If you have a lot of firms, you might first try the secret weapon, fitting a model separately for each year. Or if you have a lot of years, break the data up into 5-year periods and do the above analysis separately for each half-decade. Things change over time, and I'm always wary of models with long time periods (decades or more). I see this a lot in political science, where people naively think that they can just solve all their problems with so-called "state fixed effects," as if Vermont in 1952 is anything like Vermont in 2008.

My other recommendation is to build up your model from simple parts and try to identify exactly where your procedure is blowing up. We have a graph in ARM that makes this point. (Masanao and Yu-Sung know what graph I'm talking about.)

The last time we encountered Slate columnist Shankar Vedantam was when he puzzled over why slightly more than half of voters planned to vote for Republican candidates, given that polls show that Americans dislike the Republican Party even more than they dislike the Democrats. Vedantam attributed the new Republican majority to irrationality and "unconscious bias." But, actually, this voting behavior is perfectly consistent with there being some moderate voters who prefer divided government. The simple, direct explanation (which Vedantam mistakenly dismisses) actually works fine.

I was flipping through Slate today and noticed a new article by Vedantam headlined, "If parenthood sucks, why do we love it? Because we're addicted." I don't like this one either.