A simple and useful regression model for underdispersed count data based on Bernoulli-Poisson convolution
From MaRDI portal
Publication:2151690
DOI10.1007/s00362-021-01253-0OpenAlexW3194597720MaRDI QIDQ2151690
Marcelo Bourguignon, Rodrigo M. R. Medeiros, Diego I. Gallardo
Publication date: 5 July 2022
Published in: Statistical Papers (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00362-021-01253-0
count dataPoisson distributionEM-algorithmregression modelsunderdispersionmean and dispersion models
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A hyper-Poisson regression model for overdispersed and underdispersed count data
- A flexible regression model for count data
- Generalized poisson regression model
- A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution
- Weighted Poisson distributions for overdispersion and underdispersion situations
- Overdispersed and underdispersed Poisson generalizations
- Local Influence for Incomplete Data Models
- Reparametrization of COM–Poisson regression models with applications in the analysis of experimental data
- From Finite Sample to Asymptotic Methods in Statistics
- An empirical model for underdispersed count data
- A comparison of various tests of normality
- Double Exponential Families and Their Use in Generalized Linear Regression
- A Characterization of Convoluted Poisson Distributions with Applications to Estimation
- Underdispersion models: Models that are “under the radar”
- Parametric modal regression with varying precision
- Integer-valued autoregressive models for counts showing underdispersion
- The Gamma-count distribution in the analysis of experimental underdispersed data
- Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts
- Extended Poisson–Tweedie: Properties and regression models for count data
- Reparameterized inverse gamma regression models with varying precision
This page was built for publication: A simple and useful regression model for underdispersed count data based on Bernoulli-Poisson convolution
