Semi-parametric extended Poisson process models for count data
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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|Item Type:||Journal Article|
|Additional Information:||For more information, please refer to the journal's website (see hypertext link) or contact the author.
Author contact details: firstname.lastname@example.org
|Keywords:||count data, over and underdispersion, covariate effects, extended Poisson process model, penalized likelihood|
|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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