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    Influence diagnostics for two-component Poisson mixture regression models: applications in public health

    Access Status
    Fulltext not available
    Authors
    Xiang, Liming
    Lee, Andy
    Yau, K.
    Fung, W.
    Date
    2005
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Xiang, Liming and Lee, Andy and Yau, Kelvin K.W. and Fung, Wing. K.. 2005. Influence diagnostics for two-component Poisson mixture regression models: applications in public health. Statistics in Medicine 24: 3053-3071.
    Source Title
    Statistics in Medicine
    DOI
    10.1002/sim.2160
    Faculty
    School of Public Health
    Division of Health Sciences
    Remarks

    Copyright 2005 John Wiley & Sons, Ltd.

    Please refer to the publisher for the definitive published version.

    URI
    http://hdl.handle.net/20.500.11937/25537
    Collection
    • Curtin Research Publications
    Abstract

    In many medical and health applications, Poisson mixture regression models are commonly used to analyse heterogeneous count data. Motivated by two data sets drawn from public health studies, influence diagnostics are proposed for assessing the sensitivity of the fitted two-component Poisson mixture regression models. Under various perturbations of the observed data or model assumptions, influence assessments based on the local influence approach are developed for detecting clusters and/or individual observations that impact on the estimation of model parameters. Results from studies on recurrent urinary tract infections and maternity length of stay illustrate the usefulness of the influence diagnostics.

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