Reversible jump methods for generalised linear models and generalised linear mixed models

Jonathan Forster, Roger Gill, Antony Overstall

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

A reversible jump algorithm for Bayesian model determination among generalised linear models, under relatively diffuse prior distributions for the model parameters, is proposed. Orthogonal projections of the current linear predictor are used so that knowledge from the current model parameters is used to make effective proposals. This idea is generalised to moves of a reversible jump algorithm for model determination among generalised linear mixed models. Therefore, this algorithm exploits the full flexibility available in the reversible jump method. The algorithm is demonstrated via two examples and compared to existing methods.
Original languageEnglish
Pages (from-to)107-120
Number of pages14
JournalStatistics and Computing
Volume22
Issue number1
DOIs
Publication statusPublished - Jan 2012

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