Fitting the extended poisson process model to grouped binary data |
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| Authors: | Peter J Toscas Malcolm J Faddy |
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| Institution: | (1) CSIRO Mathematical and Information Sciences, Private Bag 10, 3169 South Clayton MDC, VIC, Australia;(2) School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, 4001 Brisbane, QLD, Australia |
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| Abstract: | Summary 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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| Keywords: | Binomial distribution iterative re-weighted least squares maximum likelihood over-dispersion under-dispersion |
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