Bayesian analysis of hierarchical models for polychotomous data from a multi-stage cluster sample

dc.contributor.advisor Hal S. Stern
dc.contributor.author Schuckers, Michael
dc.contributor.department Statistics
dc.date 2018-08-23T02:32:11.000
dc.date.accessioned 2020-06-30T07:18:49Z
dc.date.available 2020-06-30T07:18:49Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 1999
dc.date.issued 1999
dc.description.abstract <p>In this thesis we present a hierarchical Bayesian methodology for analyzing polychotomous data from multi-stage cluster samples. We begin with a model for multinomial data drawn from a two-stage cluster sample of a finite population. This model is then extended to incorporate partially observed data assuming that the data are missing at random (MAR), in the terminology of Little and Rubin (1987). We next develop a model for polychotomous data collected via a three-stage cluster sample. As with the two-stage model, we describe the methodology for dealing with partially observed data assuming they are MAR. We apply these two methodologies to the 1990 Slovenian Public Opinion Survey and present the results of these analyses. Finally, we fashion a multivariate probit model for a special type of multinomial data, multivariate binary data. We then construct this model that incorporates covariate information for the case of a two-stage cluster sample. Specifically, we outline this methodology for a two-stage cluster sample. This approach also allows for the integration of missing data into the analysis if the data are MAR. For all of the above models we use Markov chain Monte Carlo techniques to simulate samples from the posterior distribution. These samples are then utilized in making inference from the models.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/rtd/12166/
dc.identifier.articleid 13165
dc.identifier.contextkey 6766902
dc.identifier.doi https://doi.org/10.31274/rtd-180813-13442
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath rtd/12166
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/65504
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/rtd/12166/r_9940238.pdf|||Fri Jan 14 19:14:25 UTC 2022
dc.subject.disciplines Statistics and Probability
dc.subject.keywords Statistics
dc.title Bayesian analysis of hierarchical models for polychotomous data from a multi-stage cluster sample
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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