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    Restricted maximum likelihood estimation of joint mean-covariance models

    Papageorgiou, Georgios (2012) Restricted maximum likelihood estimation of joint mean-covariance models. The Canadian Journal of Statistics 40 (2), pp. 225-242. ISSN 0319-5724.

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    Abstract

    The class of joint mean-covariance models uses the modified Cholesky decomposition of the within subject covariance matrix in order to arrive to an unconstrained, statistically meaningful reparameterisation. The new parameterisation of the covariance matrix has two sets of parameters that separately describe the variances and correlations. Thus, with the mean or regression parameters, these models have three sets of distinct parameters. In order to alleviate the problem of inefficient estimation and downward bias in the variance estimates, inherent in the maximum likelihood estimation procedure, the usual REML estimation procedure adjusts for the degrees of freedom lost due to the estimation of the mean parameters. Because of the parameterisation of the joint mean covariance models, it is possible to adapt the usual REML procedure in order to estimate the variance (correlation) parameters by taking into account the degrees of freedom lost by the estimation of both the mean and correlation (variance) parameters. To this end, here we propose adjustments to the estimation procedures based on the modified and adjusted profile likelihoods. The methods are illustrated by an application to a real data set and simulation studies.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Adjusted profile likelihood, Cholesky decomposition, longitudinal data, modified profile likelihood, MSC 2010: Primary 62F10, secondary 62F12
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Georgios Papageorgiou
    Date Deposited: 16 Sep 2013 09:17
    Last Modified: 01 Aug 2019 04:10
    URI: http://eprints.bbk.ac.uk/id/eprint/8104

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