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Title: On Bayesian Inference for Partially Observed Data
Author: Gill, Roger C.
ISNI:       0000 0001 3499 9383
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2007
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When making predictions, analysing incomplete data from a medical trial or drawing inference from artificially altered data, one is required to make conditional probability statements concerning unobserved individuals or data. This thesis provides a collection of statistical techniques for inference when data is only partially observed. An efficient reversible jump Markov chain Monte Carlo algorithm for generalised linear models is constructed. This provides a formal framework for Bayesian prediction under model uncertainty. The construction of the algorithm is unique, relying on a simple and novel reversible jump transformation function. The resulting algorithm is easy to implement and requires no 'expert' knowledge. An inference framework for multivariate survey data subject to non-response is provided. Deviations from a 'close to ignorable' model are permitted through realistic a-priori changes in log-odds ratios. These a-priori deviations encode the prior belief that the non-response mechanism is non-ignorable. A current disclosure control technique is studied. This technique rounds partially observed data prior to release. A Bayesian assessment of this technique is given. This requires the construction of a Metropolis-Hastings algorithm, and the algorithms irreducibility is proven and discussed.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available