Semi-parametric Extended Poisson Process Models for Count Data

Podlich, Heather, , & Smyth, Gordon (2004) Semi-parametric Extended Poisson Process Models for Count Data. Statistics and Computing, 14, pp. 311-321.

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Description

A general framework for the analysis of count data (with covariates) is proposed using formulations for the transition rates of a state-dependent birth process. The form for the transition rates incorporates covariates proportionally, with the residual distribution determined from a smooth non-parametric state-dependent form. Computation of the resulting probabilities is discussed, leading to model estimation using a penalized likelihood function. Two data sets are used as illustrative examples, one representing underdispersed Poisson-like data and the other overdispersed binomial-like data.

Impact and interest:

11 citations in Scopus
8 citations in Web of Science®
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ID Code: 8172
Item Type: Contribution to Journal (Journal Article)
Refereed: Yes
ORCID iD:
Faddy, Malcolmorcid.org/0000-0003-4359-2037
Measurements or Duration: 11 pages
Keywords: count data, covariate effects, extended Poisson process model, over and underdispersion, penalized likelihood
DOI: 10.1023/B:STCO.0000039480.66002.5a
ISSN: 0960-3174
Pure ID: 34203212
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > QUT Faculties & Divisions > Science & Engineering Faculty
Current > Research Centres > Australian Research Centre for Aerospace Automation
Copyright Owner: Consult author(s) regarding copyright matters
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Deposited On: 21 Jun 2007 00:00
Last Modified: 14 Apr 2024 15:43