IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v72y2014icp128-146.html
   My bibliography  Save this article

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

Author

Listed:
  • Drovandi, Christopher C.
  • Pettitt, Anthony N.
  • Henderson, Robert D.
  • McCombe, Pamela A.

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.

Suggested Citation

  • 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, Elsevier, vol. 72(C), pages 128-146.
  • Handle: RePEc:eee:csdana:v:72:y:2014:i:c:p:128-146
    DOI: 10.1016/j.csda.2013.11.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947313004015
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2013.11.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. J. Vermaak & C. Andrieu & A. Doucet & S. J. Godsill, 2004. "Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 785-809, November.
    3. P. G. Ridall & A. N. Pettitt & N. Friel & P. A. McCombe & R. D. Henderson, 2007. "Motor unit number estimation using reversible jump Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(3), pages 235-269, May.
    4. Nicolas Chopin & Pierre Jacob, 2010. "Free Energy Sequential Monte Carlo Application to Mixture Modelling," Working Papers 2010-34, Center for Research in Economics and Statistics.
    5. Liu, Rui-Yin & Tao, Jian & Shi, Ning-Zhong & He, Xuming, 2011. "Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1498-1508, March.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. P. Gareth Ridall & Anthony N. Pettitt & Robert D. Henderson & Pamela A. McCombe, 2006. "Motor Unit Number Estimation—A Bayesian Approach," Biometrics, The International Biometric Society, vol. 62(4), pages 1235-1250, December.
    8. repec:dau:papers:123456789/5671 is not listed on IDEAS
    9. Mark Girolami & Ben Calderhead, 2011. "Riemann manifold Langevin and Hamiltonian Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(2), pages 123-214, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Taylor, Simon A.C. & Sherlock, Chris & Ridall, Gareth & Fearnhead, Paul, 2020. "Motor unit number estimation via sequential Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
    2. Taylor, Simon A.C. & Sherlock, Chris & Ridall, Gareth & Fearnhead, Paul, 2020. "Motor unit number estimation via sequential Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    3. Carlos A. Abanto-Valle & Gabriel Rodríguez & Hernán B. Garrafa-Aragón, 2020. "Stochastic Volatility in Mean: Empirical Evidence from Stock Latin American Markets," Documentos de Trabajo / Working Papers 2020-481, Departamento de Economía - Pontificia Universidad Católica del Perú.
    4. Assaf, A. George & Tsionas, Mike & Oh, Haemoon, 2018. "The time has come: Toward Bayesian SEM estimation in tourism research," Tourism Management, Elsevier, vol. 64(C), pages 98-109.
    5. Abanto-Valle, Carlos A. & Rodríguez, Gabriel & Garrafa-Aragón, Hernán B., 2021. "Stochastic Volatility in Mean: Empirical evidence from Latin-American stock markets using Hamiltonian Monte Carlo and Riemann Manifold HMC methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 272-286.
    6. Rojas, Helder & Dias, David, 2021. "Transfer of macroeconomic shocks in stress tests modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    7. N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607, July.
    8. Helder Rojas & David Dias, 2018. "Transmission of Macroeconomic Shocks to Risk Parameters: Their uses in Stress Testing," Papers 1809.07401, arXiv.org, revised May 2019.
    9. Li, Dan & Clements, Adam & Drovandi, Christopher, 2021. "Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo," Econometrics and Statistics, Elsevier, vol. 19(C), pages 22-46.
    10. Golchi, Shirin & Campbell, David A., 2016. "Sequentially Constrained Monte Carlo," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 98-113.
    11. Axel Finke & Ruth King & Alexandros Beskos & Petros Dellaportas, 2019. "Efficient Sequential Monte Carlo Algorithms for Integrated Population Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(2), pages 204-224, June.
    12. Filippo Pagani & Martin Wiegand & Saralees Nadarajah, 2022. "An n‐dimensional Rosenbrock distribution for Markov chain Monte Carlo testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 657-680, June.
    13. Glen Livingston & Darfiana Nur, 2020. "Bayesian inference of smooth transition autoregressive (STAR)(k)–GARCH(l, m) models," Statistical Papers, Springer, vol. 61(6), pages 2449-2482, December.
    14. Jeremy Heng & Arnaud Doucet & Yvo Pokern, 2021. "Gibbs flow for approximate transport with applications to Bayesian computation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(1), pages 156-187, February.
    15. Rigat, F. & Mira, A., 2012. "Parallel hierarchical sampling: A general-purpose interacting Markov chains Monte Carlo algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1450-1467.
    16. Filippi Sarah & Barnes Chris P. & Cornebise Julien & Stumpf Michael P.H., 2013. "On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(1), pages 87-107, March.
    17. Mathias Drton & Martyn Plummer, 2017. "A Bayesian information criterion for singular models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 323-380, March.
    18. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    19. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    20. Jesse Elliott & Zemin Bai & Shu-Ching Hsieh & Shannon E Kelly & Li Chen & Becky Skidmore & Said Yousef & Carine Zheng & David J Stewart & George A Wells, 2020. "ALK inhibitors for non-small cell lung cancer: A systematic review and network meta-analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:72:y:2014:i:c:p:128-146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.