Semi-parametric extended Poisson process models for count data

Podlich, Heather M., Faddy, Malcolm J., & Smyth, Gordon K. (2004) Semi-parametric extended Poisson process models for count data. Statistics and Computing, 14(4), pp. 311-321.

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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.

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ID Code: 8172
Item Type: Journal Article
Refereed: Yes
Additional Information: For more information, please refer to the journal's website (see hypertext link) or contact the author.
Author contact details:
Keywords: count data, over and underdispersion, covariate effects, extended Poisson process model, penalized likelihood
DOI: 10.1023/B:STCO.0000039480.66002.5a
ISSN: 0960-3174
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2004 Springer
Copyright Statement: The original publication is available at SpringerLink
Deposited On: 21 Jun 2007 00:00
Last Modified: 29 Feb 2012 13:07

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