Reparametrization of COM–Poisson regression models with applications in the analysis of experimental data

DOI10.1177/1471082X19838651zbMath1482.62043arXiv1801.09795OpenAlexW2963479936WikidataQ122112933 ScholiaQ122112933MaRDI QIDQ3386476

Walmes Marques Zeviani, H. Bonat Wagner, Eduardo E. Jun. Ribeiro, Clarice Garcia Borges Demétrio, J. P. Hinde

Publication date: 4 January 2021

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1801.09795

zbMATH Keywords

count dataoverdispersionlikelihood inferenceCOM-Poissonunderdispersion

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Point estimation (62F10) Probability distributions: general theory (60E05)

Related Items (6)

A simple and useful regression model for underdispersed count data based on Bernoulli-Poisson convolution ⋮ Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches ⋮ New zero-inflated regression models with a variant of censoring ⋮ A fast look-up method for Bayesian mean-parameterised Conway-Maxwell-Poisson regression models ⋮ Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion ⋮ A simple and useful regression model for fitting count data

Uses Software

• R

Cites Work

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• An asymptotic expansion for the normalizing constant of the Conway-Maxwell-Poisson distribution
• The Gamma-count distribution in the analysis of experimental underdispersed data
• Extended Poisson–Tweedie: Properties and regression models for count data
• The Conway-Maxwell-Poisson distribution: distributional theory and approximation
• Count Data Distributions
• A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution

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