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.
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.
Page 5 you refer to some kind of parameter Beta but there is no Beta in the equations.
Very clear crisp start and very scary Fig 3.
When I was reading it, I was a bit uncomfortable with the prior assumptions matching the true distribution of parameter values – simulations under mis-matches as well seemed as important if not more important.
And then I was sent this Gustafson and Greenland paper on an unrelated manner http://arxiv.org/pdf/1010.0306
Different setting but similar findings – Bayes usually beats frequentist on "thoughtful" evaluations of coverage.
Also a bit concerned about taking sigma as known (other than as a start). I once almost got burned very badly when assuming tau was 0 and thinking it did not matter much if I took sigma as unknown versus known (and set equal to its reported estimate).
Believe it will be a problem for tau being taken as equal to something less than the true tau leading to what Meng called the non-negligibility of R http://arxiv.org/pdf/1010.0810
K?