Modelling Truncated and Clustered Count Data

Saei, Ayoub and Chambers, Ray (2005) Modelling Truncated and Clustered Count Data. Southampton, UK, Southampton Statistical Sciences Research Institute, 18pp. (S3RI Methodology Working Papers, M05/10).


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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.

Item Type: Monograph (Working Paper)
Keywords: Cluster, Poisson, Random components, REML, Truncated.
Subjects: H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 14303
Accepted Date and Publication Date:
10 February 2005Made publicly available
Date Deposited: 10 Feb 2005
Last Modified: 31 Mar 2016 11:27

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