Bayesian analysis of hierarchical heteroscedastic linear models using Dirichlet-Laplace priors

DOI

10.2991/jsta.2017.16.1.5How to use a DOI?

Keywords

Global-local priors, Heteroscedasticity, Hierarchical models, SURE estimators.

Abstract

From practical point of view, in a two-level hierarchical model, the variance of second-level usually has a tendency to change through sub-populations. The existence of this kind of local (or intrinsic ) heteroscedasticity is a major concern in the application of statistical modeling. The main purpose of this study is to construct a Bayesian methodology via shrinkage priors in order to estimate the interesting parameters under local heteroscedasticity. The suggested methodology for this issue is to use of a class of the local-global shrinkage priors, called Dirichlet-Laplace priors. The optimal posterior concentration and straightforward posterior computation are the appealing properties of these priors. Two real data sets are analyzed to illustrate the proposed methodology.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - S.K. Ghoreishi
PY  - 2017
DA  - 2017/03/01
TI  - Bayesian analysis of hierarchical heteroscedastic linear models using Dirichlet-Laplace priors
JO  - Journal of Statistical Theory and Applications
SP  - 53
EP  - 64
VL  - 16
IS  - 1
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2017.16.1.5
DO  - 10.2991/jsta.2017.16.1.5
ID  - Ghoreishi2017
ER  -