A hierarchical Bayesian regression model for the uncertain functional constraint using screened scale mixtures of Gaussian distributions. Issue 2 (3rd March 2016)

• Record Type: Journal Article
• Authors: Kim, Hea-Jung; Choi, Taeryon; Lee, Suyeon
• Is Part Of: Statistics. Volume 50:Issue 2(2016)
• Page Start: 350
• Page End: 376
• Publication Date: 2016-03-03
• Subjects: elliptically contoured distribution -- hierarchical Bayesian model -- Markov chain Monte Carlo -- rectangular screened scale mixtures -- uncertain constraint; 62F15 -- 62G05 -- 62J02; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2015.1100616
• Languages: English
• ISSNs: 0233-1888

Abstract

Abstract : This paper considers a hierarchical Bayesian analysis of regression models using a class of Gaussian scale mixtures. This class provides a robust alternative to the common use of the Gaussian distribution as a prior distribution in particular for estimating the regression function subject to uncertainty about the constraint. For this purpose, we use a family of rectangular screened multivariate scale mixtures of Gaussian distribution as a prior for the regression function, which is flexible enough to reflect the degrees of uncertainty about the functional constraint. Specifically, we propose a hierarchical Bayesian regression model for the constrained regression function with uncertainty on the basis of three stages of a prior hierarchy with Gaussian scale mixtures, referred to as a hierarchical screened scale mixture of Gaussian regression models (HSMGRM). We describe distributional properties of HSMGRM and an efficient Markov chain Monte Carlo algorithm for posterior inference, and apply the proposed model to real applications with constrained regression models subject to uncertainty.