23 June 2017 - Chaya Stern - Prior distribution for variance parameters in hierarchical models

This week Chaya Stern will be presenting the paper "Prior distribution for variance parameters in hierarchical models". This paper discusses some of the problems when choosing prior distributions and makes several recommendations for priors on hierarchical variance parameters.  

http://www.stat.columbia.edu/~gelman/research/published/taumain.pdf

Abstract:

Various noninformative prior distributions have been suggested for scale parameters in hierarchical models. We construct a new folded-noncentral-t family of conditionally conjugate priors for hierarchical standard deviation parameters, and then consider noninformative and weakly informative priors in this family. We use an example to illustrate serious problems with the inverse-gamma family of “noninformative” prior distributions. We suggest instead to use a uniform prior on the hierarchical standard deviation, using the half-t family when the number of groups is small and in other settings where a weakly informative prior is desired. We also illustrate the use of the half-t family for hierarchical modeling of multiple variance parameters such as arise in the analysis of variance.