The Stochastic EM Algorithm for Censored Mixed Models

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    1 Citation (Scopus)

    Abstract

    The Stochastic EM algorithm is a Monte Carlo method for approximating the regular EM algorithm in missing data situations where the E step is intractable. It produces a stationary Markov chain iterative sequence where each iteration is the result of applying complete data maximum likelihood estimation to a single simulation of the complete data conditional on the observed data. Analogously to other Markov chain Monte Carlo methods, the final estimate is the mean of the iterative sequence after a burn-in period. We consider a case study of the application of the Stochastic EM algorithm for censored mixed models, a computationally challenging context. The Stochastic EM algorithm is particularly simple to apply to either linear or non-linear mixed models with censoring. All that is required is a routine to simulate censored multivariate normal observations, and a routine to fit the desired uncensored mixed model. An application is presented involving repeated measures of HIV viral load subject to censoring caused by a lower detection limit of the assay. It is found that crude methods ignoring the censoring are biased compared to results from the Stochastic EM algorithm.

    Original languageEnglish
    Title of host publicationMonte Carlo and Quasi-Monte Carlo Methods 2012
    EditorsJosef Dick, Frances Y. Kuo, Gareth W. Peters, Ian H. Sloan
    Place of PublicationHeidelberg
    PublisherSpringer, Springer Nature
    Pages553-567
    Number of pages15
    Volume65
    ISBN (Electronic)9783642410956
    ISBN (Print)9783642410949
    DOIs
    Publication statusPublished - 2013
    Event10th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2012 - Sydney, NSW, Australia
    Duration: 13 Feb 201217 Feb 2012

    Other

    Other10th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2012
    CountryAustralia
    CitySydney, NSW
    Period13/02/1217/02/12

    Fingerprint

    Dive into the research topics of 'The Stochastic EM Algorithm for Censored Mixed Models'. Together they form a unique fingerprint.

    Cite this