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


Autoria(s): Drovandi, Christopher C.; Pettitt, Anthony N.; Henderson, Robert D.; McCombe, Pamela A.
Data(s)

2014

Resumo

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).

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/54864/

Publicador

Elsevier BV

Relação

http://eprints.qut.edu.au/54864/1/MUNE_Marginalisation.pdf

DOI:10.1016/j.csda.2013.11.003

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.

Direitos

Copyright 2014 Elsevier

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

Fonte

School of Mathematical Sciences; Science & Engineering Faculty

Palavras-Chave #010400 STATISTICS #Amyotrophic lateral sclerosis #Marginalisation #Markov chain Monte Carlo #Model choice #Motor Neurone disease #Motor unit number estimation #Neurophysiology #Reversible jump
Tipo

Journal Article