NON-LOCAL PRIORS FOR HIGH-DIMENSIONAL ESTIMATION.

ABSTRACT: Jointly achieving parsimony and good predictive power in high dimensions is a main challenge in statistics. Non-local priors (NLPs) possess appealing properties for model choice, but their use for estimation has not been studied in detail. We show that for regular models NLP-based Bayesian model averaging (BMA) shrink spurious parameters either at fast polynomial or quasi-exponential rates as the sample size n increases, while non-spurious parameter estimates are not shrunk. We extend some results to linear models with dimension p growing with n. Coupled with our theoretical investigations, we outline the constructive representation of NLPs as mixtures of truncated distributions that enables simple posterior sampling and extending NLPs beyond previous proposals. Our re

SUBMITTER: Rossell D 

PROVIDER: S-EPMC5988374 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature