Imputation of missing values using quantile regression

dc.contributor.advisor Cindy L. Yu
dc.contributor.author Chen, Senniang
dc.contributor.department Statistics
dc.date 2018-08-11T12:03:11.000
dc.date.accessioned 2020-06-30T02:53:08Z
dc.date.available 2020-06-30T02:53:08Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2014
dc.date.embargo 2015-07-30
dc.date.issued 2014-01-01
dc.description.abstract <p>In this thesis, we consider an imputation method to handle missing response values based on quantile regression estimation. In the proposed method, the missing response values are generated using the estimated conditional quantile regression function at given values of covariates parametrically or semiparametrically. We adopt the generalized method of moments and the empirical likelihood method for estimation of parameters defined through a general estimating equation. We demonstrate that the proposed estimators, which combine both quantile regression imputation (parametric or semiparametric) and general estimating equation methods</p> <p>(generalized method of moments or empirical likelihood), have competitive advantages over some of the most widely used parametric and non-parametric imputation estimators. The consistency and the asymptotic normality of our estimators are established and variance estimation is provided. Results from a limited simulation study and an empirical study are presented to</p> <p>show the adequacy of the proposed methods.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/13924/
dc.identifier.articleid 4931
dc.identifier.contextkey 6199643
dc.identifier.doi https://doi.org/10.31274/etd-180810-3487
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/13924
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/28111
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/13924/Chen_iastate_0097E_14324.pdf|||Fri Jan 14 20:04:17 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Empirical likelihood
dc.subject.keywords Generalized method of moments
dc.subject.keywords Imputation
dc.subject.keywords Quantile regression
dc.title Imputation of missing values using quantile regression
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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