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    Modelling bivariate count series with excess zeros

    Access Status
    Fulltext not available
    Authors
    Lee, Andy
    Wang, Kui
    Carrivick, Philip
    Yau, K.
    Stevenson, M.
    Date
    2005
    Type
    Journal Article
    
    Metadata
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    Citation
    Lee, Andy and Wang, Kui and Carrivick, Philip and Yau, Kelvin K.W. and Stevenson, Mark. R.. 2005. Modelling bivariate count series with excess zeros. Mathematical Biosciences 196: 226-237.
    Source Title
    Mathematical Biosciences
    DOI
    10.1016/j.mbs.2005.05.001
    Faculty
    School of Public Health
    Division of Health Sciences
    URI
    http://hdl.handle.net/20.500.11937/27575
    Collection
    • Curtin Research Publications
    Abstract

    Bivariate time series of counts with excess zeros relative to the Poisson process are common in many bioscience applications. Failure to account for the extra zeros in the analysis may result in biased parameter estimates and misleading inferences. A class of bivariate zero-inflated Poisson autoregression models is presented to accommodate the zero-inflation and the inherent serial dependency between successive observations. An autoregressive correlation structure is assumed in the random component of the compound regression model. Parameter estimation is achieved via an EM algorithm, by maximizing an appropriate log-likelihood function to obtain residual maximum likelihood estimates. The proposed method is applied to analyze a bivariate series from an occupational health study, in which the zero-inflated injury count events are classified as either musculoskeletal or non-musculoskeletal in nature. The approach enables the evaluation of the effectiveness of a participatory ergonomics intervention at the population level, in terms of reducing the overall incidence of lost-time injury and a simultaneous decline in the two mean injury rates.

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