An approximate Bayesian approach to regression estimation with many auxiliary variables Sugasawa, Shonosuke Kim, Jae Kwang Kim, Jae Kwang
dc.contributor.department Statistics 2019-09-22T10:54:07.000 2020-07-02T06:57:34Z 2020-07-02T06:57:34Z Tue Jan 01 00:00:00 UTC 2019 2019-06-12
dc.description.abstract <p>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.</p>
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dc.identifier archive/
dc.identifier.articleid 1276
dc.identifier.contextkey 15181327
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/269
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 23:04:28 UTC 2022
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Generalized regression estimation
dc.subject.keywords Regularization
dc.subject.keywords Shrinkage prior
dc.subject.keywords Survey Sampling
dc.title An approximate Bayesian approach to regression estimation with many auxiliary variables
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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