A Marginalized Model for Zero-Inflated, Overdispersed, and Correlated Count Data


Abstract


Iddi and Molenberghs (2012) merged the attractive features of the so-called combined model of Molenberghs {\em et al\/} (2010) and the marginalized model of Heagerty (1999) for hierarchical non-Gaussian data with overdispersion. In this model, the fixed-effect parameters retain their marginal interpretation. Lee et al (2011) also developed an extension of Heagerty (1999) to handle zero-inflation from count data, using the hurdle model. To bring together all of these features, a marginalized, zero-inflated, overdispersed model for correlated count data is proposed. Using two empirical sets of data, it is shown that the proposed model leads to important improvements in model fit.

DOI Code: 10.1285/i20705948v6n2p149

Keywords: Marginal multilevel model; Maximum likelihood estimation; Random effects model; Negative binomial; Overdispersion; Partial Marginalization; Poisson model; Zero-Inflation.

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