Beta-negative binomial nonlinear spatio-temporal random effects modeling of COVID-19 case counts in Japan

DOI10.1080/02664763.2022.2064439OpenAlexW4224326290MaRDI QIDQ6134410

Masao Ueki

Publication date: 25 July 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://figshare.com/articles/journal_contribution/Beta-negative_binomial_nonlinear_spatio-temporal_random_effects_modeling_of_COVID-19_case_counts_in_Japan/19645366

zbMATH Keywords

count time seriesCOVID-19extreme observationspatio-temporal modelingbeta-negative binomial distribution

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Power-law models for infectious disease spread
• One mixed negative binomial distribution with application
• Modelling heavy-tailedness in count time series
• Universal residuals: a multivariate transformation
• Modeling seasonality in space-time infectious disease surveillance data
• A statistical framework for the analysis of multivariate infectious disease surveillance counts
• Beta–Negative Binomial Auto-Regressions for Modelling Integer-Valued Time Series with Extreme Observations
• Extended Poisson–Tweedie: Properties and regression models for count data
• Univariate Discrete Distributions
• Predictive Model Assessment for Count Data