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
1 December 2010
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
Text
thesis_Grapsa.pdf
- Other
More information
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
Catalogue record
Date deposited: 21 Sep 2011 10:38
Last modified: 15 Mar 2024 02:46
Export record
Contributors
Author:
Erofili Grapsa
Thesis advisor:
Jonathan J. Forster
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