Saei, Ayoub and Chambers, Ray
Modelling Truncated and Clustered Count Data , Southampton, UK Southampton Statistical Sciences Research Institute 18pp.
(S3RI Methodology Working Papers, M05/10).
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.
||Cluster, Poisson, Random components, REML, Truncated.
|10 February 2005||Published|
||10 Feb 2005
||16 Apr 2017 23:43
|Further Information:||Google Scholar|
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