An Information Matrix Prior for Bayesian Analysis in Generalized Linear Models with High Dimensional Data.

ABSTRACT: An important challenge in analyzing high dimensional data in regression settings is that of facing a situation in which the number of covariates p in the model greatly exceeds the sample size n (sometimes termed the "p > n" problem). In this article, we develop a novel specification for a general class of prior distributions, called Information Matrix (IM) priors, for high-dimensional generalized linear models. The priors are first developed for settings in which p < n, and then extended to the p > n case by defining a ridge parameter in the prior construction, leading to the Information Matrix Ridge (IMR) prior. The IM and IMR priors are based on a broad generalization of Zellner's g-prior for Gaussian linear models. Various theoretical properties of the prior and implied posterior are de

SUBMITTER: Gupta M 

PROVIDER: S-EPMC2909687 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature