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Bayesian analysis for categorical survey data

Bayesian analysis for categorical survey data
Bayesian analysis for categorical survey data
In this thesis, we develop Bayesian methodology for univariate and multivariate categorical survey data. The Multinomial model is used and the following problems are addressed. Limited information about the design variables leads us to model the unknown design variables taking into account the sampling scheme. Random effects are incorporated in the model to deal with the effect of sampling design, that produces the Multinomial GLMM and issues such as model comparison and model averaging are also discussed. The methodology is applied in a true dataset and estimates for population counts are obtained
Grapsa, Erofili
46998c2d-9d43-45e4-b83c-f3a7930c05f5
Grapsa, Erofili
46998c2d-9d43-45e4-b83c-f3a7930c05f5
Forster, Jonathan J.
e3c534ad-fa69-42f5-b67b-11617bc84879

Grapsa, Erofili (2010) Bayesian analysis for categorical survey data. University of Southampton, Mathematics: Statistics, Doctoral Thesis, 152pp.

Record type: Thesis (Doctoral)

Abstract

In this thesis, we develop Bayesian methodology for univariate and multivariate categorical survey data. The Multinomial model is used and the following problems are addressed. Limited information about the design variables leads us to model the unknown design variables taking into account the sampling scheme. Random effects are incorporated in the model to deal with the effect of sampling design, that produces the Multinomial GLMM and issues such as model comparison and model averaging are also discussed. The methodology is applied in a true dataset and estimates for population counts are obtained

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Published date: 1 December 2010
Organisations: University of Southampton, Statistics

Identifiers

Local EPrints ID: 197303
URI: http://eprints.soton.ac.uk/id/eprint/197303
PURE UUID: be6d2b39-500d-42b8-9a98-ff04282805f8
ORCID for Jonathan J. Forster: ORCID iD orcid.org/0000-0002-7867-3411

Catalogue record

Date deposited: 21 Sep 2011 10:38
Last modified: 30 Jan 2020 01:27

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Contributors

Author: Erofili Grapsa
Thesis advisor: Jonathan J. Forster ORCID iD

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