QUT ePrints

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.

View at publisher

Abstract

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:

7 citations in Scopus
Search Google Scholar™
7 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 8172
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: m.faddy@qut.edu.au
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 http://www.springerlink.com
Deposited On: 21 Jun 2007
Last Modified: 29 Feb 2012 23:07

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page