Document Type
Article
Publication Date
6-1-2026
Abstract
We developed a class of multivariate integer-valued time series models using copula theory. Each count time series is modeled as a Markov chain, with serial dependence characterized through copula-based transition probabilities for Poisson and negative binomial marginals. Cross-sectional dependence is modeled via a trivariate Gaussian or a “t-copula”, allowing for both positive and negative correlations and providing a flexible dependence structure. Model parameters are estimated using likelihood-based inference, where the trivariate Gaussian or t-copula integrals are evaluated through standard randomized Monte Carlo methods. Simulation results, along with an analysis of annual counts of major hurricanes (Category 3+) across the North Atlantic, Eastern North Pacific, and Western North Pacific basins, demonstrate the effectiveness of the proposed model.
Recommended Citation
Fernando, D.; Wen, Y.; Jayanetti, W. A Copula-Based Framework for Multivariate Count Time Series with Mixed Marginal Distributions. Stats 2026, 9, 57. https://doi.org/10.3390/stats9030057
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Multivariate Analysis Commons, Oceanography Commons, Other Mathematics Commons, Other Oceanography and Atmospheric Sciences and Meteorology Commons, Other Statistics and Probability Commons
Comments
This article was originally published in Stats, volume 9, issue 3, in 2026. https://doi.org/10.3390/stats9030057