The University of Southampton
University of Southampton Institutional Repository

Bayesian model determination for categorical data survey

Bayesian model determination for categorical data survey
Bayesian model determination for categorical data survey
Inference for survey data needs to take account of the survey design. Failing to consider the survey design in inference may lead to misleading results. The standard analysis of categorical data, developed under the assumption of multinomial sampling, is inadequate as the commonly used sampling schemes clearly violate this assumption. Since, Kish (1965) introduced the idea of a design effect, many classical solutions have been proposed, such as, first- and second-order corrections to Pearson chi-squared, likelihood-ratio chi-squared, and Wald tests. Our objective in this thesis is to present an investigation of a Bayesian approach to the analysis of categorical survey data, arising from designs including simple random sampling, finite population sampling, stratification, and cluster sampling. We focus on Bayesian methods for model selection and model averaging, where Bayes factors and the Bayesian Information Criterion (BIC) approximation have been offered as alternative approaches. These Bayesian methods are reviewed, and comparisons made between their performance. The effect of ignoring the complex sampling design is investigated. Moreover, adjustments to the multinomial-based Bayes factor and BIC are produced and evaluated.
Al-Babtain, Abdulhakim A.
28d727ea-c188-4310-b92f-a92ce9ab6d32
Al-Babtain, Abdulhakim A.
28d727ea-c188-4310-b92f-a92ce9ab6d32

Al-Babtain, Abdulhakim A. (2001) Bayesian model determination for categorical data survey. University of Southampton, Department of Mathematics, Doctoral Thesis, 215pp.

Record type: Thesis (Doctoral)

Abstract

Inference for survey data needs to take account of the survey design. Failing to consider the survey design in inference may lead to misleading results. The standard analysis of categorical data, developed under the assumption of multinomial sampling, is inadequate as the commonly used sampling schemes clearly violate this assumption. Since, Kish (1965) introduced the idea of a design effect, many classical solutions have been proposed, such as, first- and second-order corrections to Pearson chi-squared, likelihood-ratio chi-squared, and Wald tests. Our objective in this thesis is to present an investigation of a Bayesian approach to the analysis of categorical survey data, arising from designs including simple random sampling, finite population sampling, stratification, and cluster sampling. We focus on Bayesian methods for model selection and model averaging, where Bayes factors and the Bayesian Information Criterion (BIC) approximation have been offered as alternative approaches. These Bayesian methods are reviewed, and comparisons made between their performance. The effect of ignoring the complex sampling design is investigated. Moreover, adjustments to the multinomial-based Bayes factor and BIC are produced and evaluated.

Text
00183299.pdf - Other
Restricted to Repository staff only

More information

Published date: May 2001
Organisations: University of Southampton

Identifiers

Local EPrints ID: 50629
URI: https://eprints.soton.ac.uk/id/eprint/50629
PURE UUID: d8fbfec8-c992-4ccc-90af-0da21c374289

Catalogue record

Date deposited: 06 Apr 2008
Last modified: 13 Mar 2019 20:50

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×