Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation

Drovandi, Christopher C., Pettitt, Anthony N., Henderson, Robert D., & McCombe, Pamela A. (2014) Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation. Computational Statistics & Data Analysis, 72, pp. 128-146.

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Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to loss of motor units, such as motor neurone disease. However the development of a reliable, repeatable and fast real-time MUNE method has proved elusive hitherto. Ridall et al. (2007) implement a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to produce a posterior distribution for the number of motor units using a Bayesian hierarchical model that takes into account biological information about motor unit activation. However we find that the approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. Here we focus on improved inference by marginalising over latent variables to create the likelihood. In particular we explore how this can improve the RJMCMC mixing and investigate alternative approaches that utilise the likelihood (e.g. DIC (Spiegelhalter et al., 2002)). For this model the marginalisation is over latent variables which, for a larger number of motor units, is an intractable summation over all combinations of a set of latent binary variables whose joint sample space increases exponentially with the number of motor units. We provide a tractable and accurate approximation for this quantity and also investigate simulation approaches incorporated into RJMCMC using results of Andrieu and Roberts (2009).

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ID Code: 54864
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: Amyotrophic lateral sclerosis, Marginalisation, Markov chain Monte Carlo, Model choice, Motor Neurone disease, Motor unit number estimation, Neurophysiology, Reversible jump
DOI: 10.1016/j.csda.2013.11.003
ISSN: 0167-9473
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Elsevier
Copyright Statement: This is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, [VOL 72, (2014)] DOI: 10.1016/j.csda.2013.11.003
Deposited On: 19 Nov 2012 04:14
Last Modified: 02 May 2015 00:30

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