Fitting the extended poisson process model to grouped binary data

被引：0
|
作者：
Peter J. Toscas
Malcolm J. Faddy
机构：
[1] CSIRO Mathematical and Information Sciences,School of Mathematical Sciences
[2] Queensland University of Technology,undefined
来源：
Computational Statistics | 2005年 / 20卷
关键词：
Binomial distribution; iterative re-weighted least squares; maximum likelihood; over-dispersion; under-dispersion;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Extended Poisson process modelling allows the construction of a broad class of distributions, including distributions over-dispersed or under-dispersed relative to the binomial distribution, with the binomial distribution being a special case. In this paper an iteratively re-weighted least squares algorithm for fitting such generalised binomial distributions is presented, and is illustrated with an example.
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页码：595 / 609
页数：14
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