Postdoc opportunity for missing data imputation

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The Applied Statistics Center at Columbia University invites applications for a post-doctoral fellowship focusing on missing data and Bayesian inference. Eligible candidates have received a PhD in Statistics or related field. We will also consider candidates who have received appropriate quantitative training while earning a PhD in the social, behavioral or health/medical sciences. The successful candidate will be responsible for helping to build an innovative multiple imputation program including development of new models and diagnostics. Accordingly some expertise in Bayesian statistics is necessary. Also, strong programming experience in R is a must and facility with C++ is strongly encouraged. The position will also involve working with social science and health research practitioners, so the ability to perform interdisciplinary work is necessary.

The principal investigators on this project are Andrew Gelman, Jennifer Hill, and Peter Messeri, and we’re also working with Angela Aidala, Jane Waldfogel, Irv Garfinkel, and other researchers in social science and public health.

The Applied Statistics Center is an exciting research environment at Columbia involving many students, faculty, and postdocs in dozens of methodological and applied research projects.

The fellowship is for one year but may be extended by mutual agreement and contingent on funding considerations. Salary is commensurate with degree and experience.

Please submit the following materials electronically to Juli Simon Thomas: your letter of application, curriculum vitae, writing sample, programming sample, three letters of recommendation, and a two- to three-page research statement describing your research interests.

Backround

Some of our previous papers in this area include the following:

  • [2008] Diagnostics for multivariate imputations. {\em Applied Statistics}, to appear. (Kobi Abayomi, Andrew Gelman, and Marc Levy)

  • [2005] Multiple imputation for model checking: completed-data plots with missing and latent data. {\em Biometrics}. (Andrew Gelman, Iven Van Mechelen, Geert Verbecke, Daniel F. Heitjan, and Michel Meulders)

  • [2001] Using conditional distributions for missing-data imputation. Discussion of “Conditionally specified distributions,” by Arnold et al. {\em Statistical Science} {\bf 3}, 268–269. (Andrew Gelman and T. E. Raghunathan)

  • [1998] Not asked and not answered: multiple imputation for multiple surveys (with discussion). {\em Journal of the American Statistical Association} {\bf 93}, 846–874. (Andrew Gelman, Gary King, and Chuanhai Liu)

    Links have been fixed (thanks for pointing out, Jeremiah).

    • 5 thoughts on “Postdoc opportunity for missing data imputation”

    1. Those links are dead for me, and I was actually kind of interested in one for a research project I am working on this summer. Though now that I think about it I am sure I can find another source =)

    3. I'm looking for a primer on missing data analysis that will give me the basic concepts, what to use and when. Software I use: SPSS, JMP and Minitab. Any suggestions out there?

    4. By the time you've gone through Shafer's and Harrell's libraries here, what's really left to do? I mean, there's always more to do, but the rough, important work has been codified, it seems to me.

    5. Wcw,

      See the links above. When I have missing data and need to do something, the standard packages don't seem to do the trick. Much of our research involves developing tools to build confidence in imputations, wherever they may come from.

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