Semi-parametric Extended Poisson Process Models for Count Data
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.
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ID Code: | 8172 | ||
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Item Type: | Contribution to Journal (Journal Article) | ||
Refereed: | Yes | ||
ORCID iD: |
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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 |
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Copyright Owner: | Consult author(s) regarding copyright matters | ||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
Deposited On: | 21 Jun 2007 00:00 | ||
Last Modified: | 14 Apr 2024 15:43 |
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