Modelling Truncated and Clustered Count Data

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Description/Abstract

Count response data often exhibit departures from the assumptions of standard Poisson generalized linear models (McCullagh & Nelder 1989). In particular, cluster level correlation of the data and truncation at zero are two common characteristics of such data. In this paper we describe a random components truncated Poisson model that can be applied to clustered and zero-truncated count data. Residual maximum likelihood method estimators for the parameters of this model are developed and their use illustrated using a data set of non-zero counts of sheets with edge strain defects in iron sheets produced by the Mobarekeh Steel Complex, Iran. We also report on a small scale simulation study that supports the estimation procedure.