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    Multivariate generalized linear mixed models with semi-nonparametric and smooth nonparametric random effects densities

    Papageorgiou, Georgios and Hinde, J. (2012) Multivariate generalized linear mixed models with semi-nonparametric and smooth nonparametric random effects densities. Statistics and Computing 22 (1), pp. 79-92. ISSN 0960-3174.

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    Abstract

    We extend the family of multivariate generalized linear mixed models to include random effects that are generated by smooth densities. We consider two such families of densities, the so-called semi-nonparametric (SNP) and smooth nonparametric (SMNP) densities. Maximum likelihood estimation, under either the SNP or the SMNP densities, is carried out using a Monte Carlo EM algorithm. This algorithm uses rejection sampling and automatically increases the MC sample size as it approaches convergence. In a simulation study we investigate the performance of these two densities in capturing the true underlying shape of the random effects distribution. We also examine the implications of misspecification of the random effects distribution on the estimation of the fixed effects and their standard errors. The impact of the assumed random effects density on the estimation of the random effects themselves is investigated in a simulation study and also in an application to a real data set.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Longitudinal data, Mixed models, Multinomial responses, Random effects, Semi-nonparametric densities, Smooth nonparametric densities
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Economics, Mathematics and Statistics
    Depositing User: Georgios Papageorgiou
    Date Deposited: 16 Sep 2013 09:08
    Last Modified: 29 Jul 2019 15:23
    URI: http://eprints.bbk.ac.uk/id/eprint/8106

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