B1507
Title: A comprehensive Bayesian framework for envelope models
Authors: Saptarshi Chakraborty - State University of New York at Buffalo (United States)
Zhihua Su - University of Florida (United States)
Zhihua Su - University of Florida (United States) [presenting]
Abstract: The envelope model aims to increase efficiency in multivariate analysis by using dimension reduction techniques. It has been used in many contexts, including linear regression, generalized linear models, matrix/tensor variate regression, reduced rank regression, and quantile regression. It has shown the potential to provide substantial efficiency gains. Virtually all of these advances, however, have been made from a frequentist perspective, and the literature addressing envelope models from a Bayesian point of view is sparse. The objective of this article is to propose a Bayesian framework that is applicable across various envelope model contexts. The proposed framework aids straightforward interpretation of model parameters and allows easy incorporation of prior information. A simple block Metropolis-within-Gibbs MCMC sampler is provided for practical implementations of the method. Simulations and data examples are included for illustration. Supplementary materials for this article are available online.
