A Conway-Maxwell-Poisson GARMA Model for Count Data

2019/01/22 by Ricardo S. Ehlers, Ehlers, Ricardo S
Computer Science · Mathematics · Social Sciences · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #Transportation Planning and Optimization

paper · pdf · doi:10.48550/arxiv.1901.07473

openalex publication_date 2019/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a flexible model for count time series which has potential uses for both underdispersed and overdispersed data. The model is based on the Conway-Maxwell-Poisson (COM-Poisson) distribution with parameters varying along time to take serial correlation into account. Model estimation is challenging however and require the application of recently proposed methods to deal with the intractable normalising constant as well as efficiently sampling values from the COM-Poisson distribution.

Related

• A Time-Series Model for Underdispersed or Overdispersed Counts
• Mean-parametrized Conway-Maxwell-Poisson regression models for dispersed counts
• Analyzing count data using a time series model with an exponentially decaying covariance structure
• A fast look-up method for Bayesian mean-parameterised Conway–Maxwell–Poisson regression models
• A Tree-based Semi-Varying Coefficient Model for the COM-Poisson Distribution