Post stratification

Making the optimal beverage from the fruit life gave you

A trick for handling a non-random sampling problem particularly common in survey data.

MRP, a.k.a. Mister P, is one method for correcting for non-response bias and other such sampling nastiness. Dan Simpson explains MRP with structured priors.

See also the generalized version, RPP.

I have not used this tool practically and so am not at all qualified to comment.

What I can do is link to my reading list of examples and explainers:

Bob Carpenter’s worked example is interesting reading in this context, explaining Thomas Lumley’s post.

There are implementations also by Adam Haber using Tensorflow Probability and Lauren Kennedy and Jonah Gabry in rstanarm.


Bareinboim, Elias, and Judea Pearl. 2016. Causal Inference and the Data-Fusion Problem.” Proceedings of the National Academy of Sciences 113 (27): 7345–52.
Gao, Yuxiang, Lauren Kennedy, Daniel Simpson, and Andrew Gelman. 2019. Improving Multilevel Regression and Poststratification with Structured Priors.” arXiv:1908.06716 [Stat], August.
Gelman, Andrew. 2007. Struggles with Survey Weighting and Regression Modeling.” Statistical Science 22 (2): 153–64.
Gelman, Andrew, and John B. Carlin. 2000. Poststratification and Weighting Adjustments.” In In. Wiley.
Kennedy, Edward H., Jacqueline A. Mauro, Michael J. Daniels, Natalie Burns, and Dylan S. Small. 2019. Handling Missing Data in Instrumental Variable Methods for Causal Inference.” Annual Review of Statistics and Its Application 6 (1): 125–48.
Krivitsky, Pavel N., and Martina Morris. 2017. Inference For Social Network Models From Egocentrically Sampled Data, With Application To Understanding Persistent Racial Disparities In Hiv Prevalence In The Us.” The Annals of Applied Statistics 11 (1): 427–55.
Little, R. J. A. 1993. Post-Stratification: A Modeler’s Perspective.” Journal of the American Statistical Association 88 (423): 1001–12.
Little, Roderick JA. 1991. “Inference with Survey Weights.” Journal of Official Statistics 7 (4): 405.

No comments yet. Why not leave one?

GitHub-flavored Markdown & a sane subset of HTML is supported.