Modelling Truncated and Clustered Count Data
Modelling Truncated and Clustered Count Data
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.
Southampton Statistical Sciences Research Institute, University of Southampton
Saei, Ayoub
d9202095-5650-4b3d-9b13-a8d16e10b338
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
10 February 2005
Saei, Ayoub
d9202095-5650-4b3d-9b13-a8d16e10b338
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Saei, Ayoub and Chambers, Ray
(2005)
Modelling Truncated and Clustered Count Data
(S3RI Methodology Working Papers, M05/10)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
18pp.
Record type:
Monograph
(Working Paper)
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.
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s3ri-workingpaper-m05-10.pdf
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Published date: 10 February 2005
Keywords:
Cluster, Poisson, Random components, REML, Truncated.
Identifiers
Local EPrints ID: 14303
URI: http://eprints.soton.ac.uk/id/eprint/14303
PURE UUID: 9f44832a-dff9-4b97-84f1-64b8030b07c2
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Date deposited: 10 Feb 2005
Last modified: 20 Feb 2024 03:20
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Contributors
Author:
Ayoub Saei
Author:
Ray Chambers
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