Kernel deconvolution density estimation

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dc.contributor.advisor Alicia L. Carriquiry
dc.contributor.advisor Daniel J. Nordman
dc.contributor.author Basulto-Elias, Guillermo
dc.contributor.department Statistics
dc.date 2018-08-11T06:17:42.000
dc.date.accessioned 2020-06-30T03:07:05Z
dc.date.available 2020-06-30T03:07:05Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2016
dc.date.embargo 2017-08-14
dc.date.issued 2016-01-01
dc.description.abstract <p>This dissertation is about kernel deconvolution density estimation (KDDE), which is nonparametric density estimation based on a sample contaminated with measurement error. It is separated in four parts. First we explore some methodological aspects of KDDE. In the following two parts we describe the computational challenges in KDDE and our statistical software for KDDE in R. Finally, we propose a simple bandwidth selection procedure that has good theoretical properties.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/15874/
dc.identifier.articleid 6881
dc.identifier.contextkey 11169178
dc.identifier.doi https://doi.org/10.31274/etd-180810-5501
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/15874
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/30057
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/15874/BasultoElias_iastate_0097E_16029.pdf|||Fri Jan 14 20:47:47 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords bandwidth
dc.subject.keywords deconvolution
dc.subject.keywords density
dc.subject.keywords flat-top
dc.subject.keywords kernel
dc.title Kernel deconvolution density estimation
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
thesis.degree.discipline Statistics
thesis.degree.level dissertation
thesis.degree.name Doctor of Philosophy
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