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Topics in design-based and Bayesian inference for surveys

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dc.contributor.authorHernandez-Stumpfhauser, Daniel, author
dc.contributor.authorOpsomer, Jean, advisor
dc.contributor.authorBreidt, F. Jay, committee member
dc.contributor.authorHoeting, Jennifer A., committee member
dc.contributor.authorKreidenweis, Sonia M., committee member
dc.date.accessioned2007-01-03T08:26:21Z
dc.date.available2014-01-01T08:10:42Z
dc.date.issued2012
dc.description.abstractWe deal with two different topics in Statistics. The first topic in survey sampling deals with variance and variance estimation of estimators of model parameters in the design-based approach to analytical inference for survey data when sampling weights include post-sampling weight adjustments such as calibration. Under the design-based approach estimators of model parameters, if available in closed form, are written as functions of estimators of population totals and means. We examine properties of these estimators in particular their asymptotic variances and show how ignoring the post-sampling weight adjustments, i.e. treating sampling weights as inverses of inclusion probabilities, results in biased variance estimators. Two simple simulation studies for two common estimators, an estimator of a population ratio and an estimator of regression coefficients, are provided with the purpose of showing situations for which ignoring the post-sampling weight adjustments results in significant biased variance estimators. For the second topic we consider Bayesian inference for directional data using the projected normal distribution. We show how the models can be estimated using Markov chain Monte Carlo methods after the introduction of suitable latent variables. The cases of random sample, regression, model comparison and Dirichlet process mixture models are covered and motivated by a very large dataset of daily departures of anglers. The number of parameters increases with sample size and thus the need of exploring alternatives. We explore mean field variational methods and identify a number of problems in the application of the method to these models, caused by the poor approximation of the variational distribution to the posterior distribution. We propose solutions to those problems by improving the mean field variational approximation through the use of the Laplace approximation for the regression case and through the use of novel Monte Carlo procedures for the mixture model case.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierHernandezStumpfhauser_colostate_0053A_11534.pdf
dc.identifierETDF2012500305STAT
dc.identifier.urihttp://hdl.handle.net/10217/71564
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2000-2019
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectvariance estimation
dc.subjectDirichlet process
dc.subjectdirectional data
dc.subjectBayesian analysis
dc.titleTopics in design-based and Bayesian inference for surveys
dc.typeText
dcterms.embargo.expires2014-01-01
dcterms.embargo.terms2014-01-01
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineStatistics
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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