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    Minimum hellinger distance estimation for k-component Poisson mixture with random effects

    Access Status
    Fulltext not available
    Authors
    Xiang, L.
    Yau, K. K. W.
    Hui, Y. V.
    Lee, Andy
    Date
    2008
    Type
    Journal Article
    
    Metadata
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    Citation
    Xiang, Liming and Yau, Kelvin K.W. and Hui, Yerr Van and Lee, Andy H. 2008. Minimum hellinger distance estimation for k-component poisson mixture with random effects. Biometrics 64 (2): pp. 508-518.
    Source Title
    Biometrics
    DOI
    10.1111/j.1541-0420.2007.00920.x
    ISSN
    0006-341X
    Faculty
    Faculty of Health Sciences
    Nursing and Midwifery
    Western Australian Centre for Cancer and Palliative Care (WACCP)
    School
    Epidemiology and Biostatistics
    Remarks

    Copyright © 2008 John Wiley & Sons, Ltd.

    URI
    http://hdl.handle.net/20.500.11937/8902
    Collection
    • Curtin Research Publications
    Abstract

    Summary. The k-component Poisson regression mixture with random effects is an effective model in describing the heterogeneity for clustered count data arising from several latent subpopulations. However, the residual maximum likelihood estimation (REML) of regression coefficients and variance component parameters tend to be unstable and may result in misleading inferences in the presence of outliers or extreme contamination. In the literature, the minimum Hellinger distance (MHD) estimation has been investigated to obtain robust estimation for finite Poisson mixtures. This article aims to develop a robust MHD estimation approach for k-component Poisson mixtures with normally distributed random effects. By applying the Gaussian quadrature technique to approximate the integrals involved in the marginal distribution, the marginal probability function of the k-component Poisson mixture with random effects can be approximated by the summation of a set of finite Poisson mixtures. Simulation study shows that the MHD estimates perform satisfactorily for data without outlying observation(s), and outperform the REML estimates when data are contaminated. Application to a data set of recurrent urinary tract infections (UTI) with random institution effects demonstrates the practical use of the robust MHD estimation method.

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