Characterizing Existence and Location of the ML Estimate in the Conway–Maxwell–Poisson Model
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Publication:6497055
DOI10.3103/S1066530724700042MaRDI QIDQ6497055
Stefan Bedbur, Unnamed Author, Udo Kamps
Publication date: 6 May 2024
Published in: Mathematical Methods of Statistics (Search for Journal in Brave)
Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)
Cites Work
- Unnamed Item
- Exponential-family random graph models for valued networks
- 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
- Connections of the Poisson weight function to overdispersion and underdispersion
- Information and Exponential Families
- Conway–Maxwell–Poisson seasonal autoregressive moving average model
- A Tree-Based Semi-Varying Coefficient Model for the COM-Poisson Distribution
- Bayesian Conway–Maxwell–Poisson regression models for overdispersed and underdispersed counts
- The COM‐Poisson model for count data: a survey of methods and applications
- 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
- Analyzing longitudinal clustered count data with zero inflation: Marginal modeling using the Conway–Maxwell–Poisson distribution
- Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly Intractable Bayesian Inference
- Uniformly most powerful unbiased tests for the dispersion parameter of the Conway-Maxwell-Poisson distribution
- Bayesian inference, model selection and likelihood estimation using fast rejection sampling: the Conway-Maxwell-Poisson distribution
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