Uniformly most powerful unbiased tests for the dispersion parameter of the Conway-Maxwell-Poisson distribution
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Publication:6160852
DOI10.1016/j.spl.2023.109801zbMath1524.62064OpenAlexW4319235002MaRDI QIDQ6160852
Publication date: 26 June 2023
Published in: Statistics \& Probability Letters (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.spl.2023.109801
overdispersionunderdispersionConway-Maxwell-Poisson distributionuniformly most powerful unbiased test
Parametric hypothesis testing (62F03) Generalized linear models (logistic models) (62J12) Probability distributions: general theory (60E05) Characterization and structure theory of statistical distributions (62E10)
Cites Work
- A flexible regression model for count data
- Exponential-family random graph models for valued networks
- Analysis of discrete data by Conway-Maxwell Poisson distribution
- A flexible distribution class for count data
- Efficient Bayesian inference for COM-Poisson regression models
- Bivariate Conway-Maxwell Poisson distributions with given marginals and correlation
- Conway–Maxwell–Poisson seasonal autoregressive moving average model
- A Model for Function Word Counts
- Stochastic Modeling of Scientific Data
- A Tree-Based Semi-Varying Coefficient Model for the COM-Poisson Distribution
- Bayesian Conway–Maxwell–Poisson regression models for overdispersed and underdispersed counts
- Mean-parametrized Conway–Maxwell–Poisson regression models for dispersed counts
- The COM‐Poisson model for count data: a survey of methods and applications
- Testing Statistical Hypotheses
- The Conway-Maxwell-Poisson distribution: distributional theory and approximation
- A Useful Distribution for Fitting Discrete Data: Revival of the Conway–Maxwell–Poisson Distribution
- A homogeneously weighted moving average control chart for Conway–Maxwell Poisson distribution
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