An approximate Bayesian approach to regression estimation with many auxiliary variables

Date
2019-06-12
Authors
Sugasawa, Shonosuke
Kim, Jae Kwang
Kim, Jae Kwang
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Department
Statistics
Abstract

Model-assisted estimation with complex survey data is an important practical problem in survey sampling. When there are many auxiliary variables, selecting significant variables associated with the study variable would be necessary to achieve efficient estimation of population parameters of interest. In this paper, we formulate a regularized regression estimator in the framework of Bayesian inference using the penalty function as the shrinkage prior for model selection. The proposed Bayesian approach enables us to get not only efficient point estimates but also reasonable credible intervals for population means. Results from two limited simulation studies are presented to facilitate comparison with existing frequentist methods.

Comments

This pre-print is made available through arxiv: https://arxiv.org/abs/1906.04398.

Description
Keywords
Citation
DOI
Source
Collections