Correction to: a flexible distribution class for count data

Sellers, Kimberly F.; Swift, Andrew W.; Weems, Kimberly S.

doi:10.1186/s40488-017-0078-z

J Stat Distrib App

2017

DOI: 10.1186/s40488-017-0078-z

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Correction to: a flexible distribution class for count data

Kimberly F. Sellers

Andrew W. Swift

Kimberly S. Weems

Abstract: Following publication of the original article (Sellers et al., 2017), the authors reported that the typesetters had misinterpreted some of the edits included in their proof corrections, namely instances of "sp" to denote that an extra space was required. The original article has been corrected.

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Modelling Underdispersed Count Data: Relative Performance of Poisson Model and its Alternatives

N.K.G.¹,

Ngom²,

Noba³

2022

African Journal of Mathematics and Statistics Studies

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Count data are common in many fields and often modelled with the Poisson model. However, the equidispersion assumption (variance = mean) related to the Poisson model is often violated in practice. While much research has focused on modelling overdispersed count data, underdispersion has received relatively little attention. Alternative models are therefore needed to handle overdispersion (variance > mean) and underdispersion (variance < mean). This study assessed the relative performance of the Poisson model and its alternatives (COM-Poisson, Generalized Poisson Regression, Double Poisson and Gamma Count) to model underdispersed count data. Using a Monte Carlo experiment, the simulation plan considered various underdispersion levels (k (variance/mean) = 0.2, 0.5 and 0.81), k=1 as a control, and sample sizes (n=20, 50, 100, 300 and 500). Results showed that the Poisson model is not robust to handle underdispersion but it is the best performer when k=1. The COM-Poisson model best fitted severe underdispersed data (k=0.2). It is also the best performer model for moderate underdispersed count data (k=0.81). However, when k=0.5, the Double Poisson model and Generalized Poisson model outperformed other models for relatively large sample sizes (n=100, 300 and 500). Our finding suggests that none of the models suits all situations. Therefore, in practice, several of these models need to be tested to select the best one.

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Modelling Underdispersed Count Data: Relative Performance of Poisson Model and its Alternatives

N.K.G.¹,

Ngom²,

Noba³

2022

African Journal of Mathematics and Statistics Studies

View full text Add to dashboard Cite

show abstract

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Correction to: a flexible distribution class for count data

Cited by 1 publication

References 1 publication

Modelling Underdispersed Count Data: Relative Performance of Poisson Model and its Alternatives

Modelling Underdispersed Count Data: Relative Performance of Poisson Model and its Alternatives

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