Likelihood-based modelling and analysis of data under-dispered relative to the Poisson distribution

Malcolm Faddy; RJ Bosch

doi:10.1111/j.0006-341X.2001.00620.x

Likelihood-based modelling and analysis of data under-dispered relative to the Poisson distribution

Malcolm Faddy, RJ Bosch

Mathematics

Research output: Contribution to journal › Article

19 Citations (Scopus)

Abstract

By using a generalization of the Poisson process, distributions can be constructed that show appropriate amounts of underdispersion relative to the Poisson distribution that may be apparent from observed data. These are then used to examine the differences between the distributions of numbers of fetal implants in mice corresponding to different doses of the herbicide 2,4,5-T.

Original language	English
Pages (from-to)	620-624
Number of pages	5
Journal	Biometrics
Volume	57
Issue number	2
DOIs	https://doi.org/10.1111/j.0006-341X.2001.00620.x
Publication status	Published - 1 Jun 2001

Keywords

underdispersion
toxicological response experiments
quality assurance comparisons
Markov counting process

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10.1111/j.0006-341X.2001.00620.x

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abstract = "By using a generalization of the Poisson process, distributions can be constructed that show appropriate amounts of underdispersion relative to the Poisson distribution that may be apparent from observed data. These are then used to examine the differences between the distributions of numbers of fetal implants in mice corresponding to different doses of the herbicide 2,4,5-T.",

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AB - By using a generalization of the Poisson process, distributions can be constructed that show appropriate amounts of underdispersion relative to the Poisson distribution that may be apparent from observed data. These are then used to examine the differences between the distributions of numbers of fetal implants in mice corresponding to different doses of the herbicide 2,4,5-T.

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KW - toxicological response experiments

KW - quality assurance comparisons

KW - Markov counting process

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Likelihood-based modelling and analysis of data under-dispered relative to the Poisson distribution

Abstract

Keywords

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