Accounting for model uncertainty in multiple imputation under complex sampling

dc.contributor.author Goh, Gyuhyeong
dc.contributor.author Kim, Jae Kwang
dc.contributor.author Kim, Jae Kwang
dc.contributor.department Statistics
dc.date 2019-09-18T11:06:27.000
dc.date.accessioned 2020-07-02T06:57:33Z
dc.date.available 2020-07-02T06:57:33Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.issued 2020-01-01
dc.description.abstract <p>Multiple imputation provides an effective way to handle missing data. When several possible models are under consideration for the data, the multiple imputation is typically performed under a single-best model selected from the candidate models. This single model selection approach ignores the uncertainty associated with the model selection and so leads to underestimation of the variance of multiple imputation estimator. In this paper, we propose a new multiple imputation procedure incorporating model uncertainty in the final inference. The proposed method incorporates possible candidate models for the data into the imputation procedure using the idea of Bayesian Model Averaging (BMA). The proposed method is directly applicable to handling item nonresponse in survey sampling. Asymptotic properties of the proposed method are investigated. A limited simulation study confirms that our model averaging approach provides better estimation performance than the single model selection approach.</p>
dc.description.comments <p>This is a manuscript of an article published as Goh, Gyuhyeong, and Jae Kwang Kim. "Accounting for model uncertainty in multiple imputation under complex sampling." <em>Scandinavian Journal of Statistics</em> (2020). doi: <a href="https://doi.org/10.1111/sjos.12473">10.1111/sjos.12473</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/268/
dc.identifier.articleid 1266
dc.identifier.contextkey 15163859
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/268
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90585
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/268/2018_Kim_AccountingModelPreprint.pdf|||Fri Jan 14 23:04:10 UTC 2022
dc.source.uri 10.1111/sjos.12473
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Approximate Bayesian Computation
dc.subject.keywords Bayesian Model Averaging
dc.subject.keywords Informative sampling
dc.subject.keywords Item nonresponse
dc.title Accounting for model uncertainty in multiple imputation under complex sampling
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_AccountingModelPreprint.pdf
Size:
350.2 KB
Format:
Adobe Portable Document Format
Description:
Collections