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    Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model

    Access Status
    Fulltext not available
    Authors
    Saffari, S.E.
    Adnan, R.
    Greene, William
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Saffari, S.E. and Adnan, R. and Greene, W. 2011. Handling of Over-Dispersion of Count Data via Truncation using Poisson Regression Model. Journal of Computer Science & Computational Mathematics. 1 (1): pp. 1-4.
    Source Title
    Journal of Computer Science & Computational Mathematics
    Additional URLs
    http://www.jcscm.net/fmgr/download.php?id=1705649
    ISSN
    2231-8879
    School
    School of Economics and Finance
    URI
    http://hdl.handle.net/20.500.11937/18413
    Collection
    • Curtin Research Publications
    Abstract

    A Poisson model typically is assumed for count data. It is assumed to have the same value for expectation and variance in a Poisson distribution, but most of the time there is over-dispersion in the model. Furthermore, the response variable in such cases is truncated for some outliers or large values. In this paper, a Poisson regression model is introduced on truncated data. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness-of-fit for the regression model is examined. We study the effects of truncation in terms of parameters estimation and their standard errors via real data.

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