A Random Effect Model Approach to Survey Data Integration

dc.contributor.author Gwak, Eunseon
dc.contributor.author Kim, Jae Kwang
dc.contributor.author Kim, Jae Kwang
dc.contributor.author Kim, Youngwon
dc.contributor.department Statistics
dc.date 2018-08-29T03:03:10.000
dc.date.accessioned 2020-07-02T06:56:47Z
dc.date.available 2020-07-02T06:56:47Z
dc.date.copyright Mon Jan 01 00:00:00 UTC 2018
dc.date.issued 2018-01-01
dc.description.abstract <p>Combining information from several surveys, or survey integration, is an important practical problem in survey sampling. When the samples are selected from similar but different populations, random effect models can be used to describe the sample observations and to borrow strength from multiple surveys. In this paper, we consider a prediction approach to survey integration assuming random effect models. The sampling designs are allowed to be informative. The model parameters are estimated using a version of EM algorithm accounting for the sampling design. The mean squared error estimation is also discussed. Two limited simulation studies are used to investigate the performance of the proposed method.</p>
dc.description.comments <p>This article is published as E. Gwak, J.K. Kim, and Y. Kim (2018). “A Random Effect Model Approach to Survey Data Integration," <em>Statistics and Applications </em>16, 227-243. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/143/
dc.identifier.articleid 1144
dc.identifier.contextkey 12699431
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/143
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90447
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/143/2018_Kim_RandomEffect.pdf|||Fri Jan 14 20:18:10 UTC 2022
dc.subject.disciplines Design of Experiments and Sample Surveys
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Best prediction
dc.subject.keywords small area estimation
dc.subject.keywords shrinkage method
dc.subject.keywords constrained EM algorithm
dc.title A Random Effect Model Approach to Survey Data Integration
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
File
Original bundle
Now showing 1 - 1 of 1
Name:
2018_Kim_RandomEffect.pdf
Size:
195.93 KB
Format:
Adobe Portable Document Format
Description:
Collections