On Bayesian inference for partially observed data

Abstract

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.

More information

Identifiers

Catalogue record

Export record

ASCII CitationAtomBibTeXData Cite XMLDublin CoreDublin CoreEP3 XMLEndNoteHTML CitationHTML CitationHTML ListJSONMETSMODSMPEG-21 DIDLOpenURL ContextObjectOpenURL ContextObject in SpanRDF+N-TriplesRDF+N3RDF+XMLRIOXX2 XMLReferReference ManagerSimple Metadata