An approximate Bayesian approach to regression estimation with many auxiliary variables
dc.contributor.author | Sugasawa, Shonosuke | |
dc.contributor.author | Kim, Jae Kwang | |
dc.contributor.department | Statistics (LAS) | |
dc.date | 2019-09-22T10:54:07.000 | |
dc.date.accessioned | 2020-07-02T06:57:34Z | |
dc.date.available | 2020-07-02T06:57:34Z | |
dc.date.copyright | Tue Jan 01 00:00:00 UTC 2019 | |
dc.date.issued | 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> | |
dc.description.comments | <p>This pre-print is made available through arxiv: <a href="https://arxiv.org/abs/1906.04398">https://arxiv.org/abs/1906.04398</a>.</p> | |
dc.format.mimetype | application/pdf | |
dc.identifier | archive/lib.dr.iastate.edu/stat_las_pubs/269/ | |
dc.identifier.articleid | 1276 | |
dc.identifier.contextkey | 15181327 | |
dc.identifier.s3bucket | isulib-bepress-aws-west | |
dc.identifier.submissionpath | stat_las_pubs/269 | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/90586 | |
dc.language.iso | en | |
dc.source.bitstream | archive/lib.dr.iastate.edu/stat_las_pubs/269/2019_Kim_ApproximateBayesianPreprint.pdf|||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 | |
relation.isAuthorOfPublication | fdf914ae-e48d-4f4e-bfa2-df7a755320f4 | |
relation.isOrgUnitOfPublication | 264904d9-9e66-4169-8e11-034e537ddbca |
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