Nonparametric Imputation of Missing Values for Estimating Equation Based Inference Wang, Dong Chen, Song
dc.contributor.department Statistics 2018-02-16T19:07:26.000 2020-07-02T06:56:11Z 2020-07-02T06:56:11Z 2005-04-01
dc.description.abstract <p>We propose a nonparametric imputation procedure for data with missing values and establish an empirical likelihood inference for parameters defined by general estimating equations. The imputation is carried out multiple times via a nonparametric estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a profile likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can remove the selection bias in the missingness and the empirical likelihood leads to more efficient parameter estimation. The proposed method is evaluated by simulation and an empirical study on the relationship between eye weight and gene transcriptional abundance of recombinant inbred mice.</p>
dc.description.comments <p>This preprint was published as Dong Wang and Song Xi Chen, "Empirical Likelihood for Estimating Equations With Missing Values", <em>Annals of Statistics</em> (2009): 490-517, doi: <a href="" target="_blank">10.1214/07-AOS585</a></p>
dc.identifier archive/
dc.identifier.articleid 1043
dc.identifier.contextkey 7332138
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_preprints/41
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 00:09:35 UTC 2022
dc.source.uri 10.1214/07-AOS585
dc.subject.disciplines Statistics and Probability
dc.subject.keywords empirical likelihood
dc.subject.keywords estimating equations
dc.subject.keywords kernel estimation
dc.subject.keywords missing values
dc.subject.keywords nonparametric imputation
dc.title Nonparametric Imputation of Missing Values for Estimating Equation Based Inference
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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