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2015•Unpublished venueRequires access

COMPARISON COUNT REGRESSION MODELS FOR OVERDISPERSED

Sibel Alturk, Elif Neyran Soylu

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Abstract

Count data become widely available in many diciplines. The most popular distribution for modeling count data is the Poisson distribution which assume equidispersion (Variance is equal to the mean). Since observed count data often exhibit over or under dispersion, Poisson models become less ideal for modeling. To deal with a wide range of dispersion levels, Quasi Poisson regresion, Negative Binomial regression and lately Conway-Maxwell-Poisson (COM-Poisson) regression used as an alternative regression models. We compare the COM-Poisson to all other regression models and illustrate its advantage and usefulness using over-dispersed alga data.

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What this paper is about

Count data become widely available in many diciplines. The most popular distribution for modeling count data is the Poisson distribution which assume equidispersion (Variance is equal to the mean). Since observed count data often exhibit over or under dispersion, Poisson models become less ideal for modeling. To deal with a wide range of dispersion levels, Quasi Poisson regresion, Negative Binomial regression and lately Conway-Maxwell-Poisson (COM-Poisson) regression used as an alternative regression models. We compare the COM-Poisson to all other regression models and illustrate its advantage and usefulness using over-dispersed alga data.

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Available abstract

Count data become widely available in many diciplines. The most popular distribution for modeling count data is the Poisson distribution which assume equidispersion (Variance is equal to the mean). Since observed count data often exhibit over or under dispersion, Poisson models become less ideal for modeling. To deal with a wide range of dispersion levels, Quasi Poisson regresion, Negative Binomial regression and lately Conway-Maxwell-Poisson (COM-Poisson) regression used as an alternative regression models. We compare the COM-Poisson to all other regression models and illustrate its advantage and usefulness using over-dispersed alga data.

Key concepts: Count data, Poisson regression, Negative binomial distribution, Poisson distribution, Overdispersion, Quasi-likelihood, Statistics, Mathematics

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