Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

6-12-2019

Journal or Book Title

arxiv

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.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Share

COinS