Semi-parametric extended Poisson process models for count data

被引：0
作者：
Heather M. Podlich
Malcolm J. Faddy
Gordon K. Smyth
机构：
[1] The University of Queensland,School of Land and Food Sciences
[2] Queensland University of Technology,School of Mathematical Sciences
[3] Walter and Eliza Hall Institute of Medical Research,undefined
来源：
Statistics and Computing | 2004年 / 14卷
关键词：
count data; over and underdispersion; covariate effects; extended Poisson process model; penalized likelihood;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：311 / 321
页数：10
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