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

Drovandi, Christopher C., Pettitt, Anthony N., Henderson, Robert D. and McCombe, Pamela A. (2014) Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation. Computational Statistics and Data Analysis, 72 128-146. doi:10.1016/j.csda.2013.11.003

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Author Drovandi, Christopher C.
Pettitt, Anthony N.
Henderson, Robert D.
McCombe, Pamela A.
Title Marginal reversible jump Markov chain Monte Carlo with application to motor unit number estimation
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
Publication date 2014-04-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.csda.2013.11.003
Open Access Status Not yet assessed
Volume 72
Start page 128
End page 146
Total pages 19
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Subject 2613 Statistics and Probability
1703 Computational Theory and Mathematics
2605 Computational Mathematics
2604 Applied Mathematics
Abstract Motor unit number estimation (MUNE) is a method which aims to provide a quantitative indicator of progression of diseases that lead to a 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. Previously, a reversible jump Markov chain Monte Carlo (RJMCMC) algorithm has been implemented 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 this approach can be unreliable for some datasets since it can suffer from poor cross-dimensional mixing. The focus is on improved inference by marginalising over latent variables to create the likelihood. More specifically, the emphasis is on how this marginalisation can improve the RJMCMC mixing and that alternative approaches that utilise the likelihood (e.g. DIC) can be investigated. 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. A tractable and accurate approximation for this quantity is provided and also other approximations based on Monte Carlo estimates that can be incorporated into RJMCMC are investigated.
Keyword Marginalisation
Model choice
Motor neurone disease
Motor unit number estimation
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online 9 November 2013

Document type: Journal Article
Sub-type: Article (original research)
Collections: UQ Centre for Clinical Research Publications
Official 2014 Collection
School of Medicine Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
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Created: Tue, 17 Dec 2013, 10:15:11 EST by System User on behalf of UQ Centre for Clinical Research