A class of copula-based bivariate poisson time series models with applications
M Alqawba, D Fernando, N Diawara - Computation, 2021 - mdpi.com
A class of bivariate integer-valued time series models was constructed via copula theory.
Each series follows a Markov chain with the serial dependence captured using copula-
based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins.
The copula theory was also used again to capture the dependence between the two series
using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible
dependence structure that allows for positive and negative correlation, as well. In addition …
Each series follows a Markov chain with the serial dependence captured using copula-
based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins.
The copula theory was also used again to capture the dependence between the two series
using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible
dependence structure that allows for positive and negative correlation, as well. In addition …
[PDF][PDF] A Class of Copula-Based Bivariate Poisson Time Series Models with Applications. Computation 2021, 9, 108
M Alqawba, D Fernando, N Diawara - 2021 - academia.edu
A class of bivariate integer-valued time series models was constructed via copula theory.
Each series follows a Markov chain with the serial dependence captured using copula-
based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins.
The copula theory was also used again to capture the dependence between the two series
using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible
dependence structure that allows for positive and negative correlation, as well. In addition …
Each series follows a Markov chain with the serial dependence captured using copula-
based transition probabilities from the Poisson and the zero-inflated Poisson (ZIP) margins.
The copula theory was also used again to capture the dependence between the two series
using either the bivariate Gaussian or “t-copula” functions. Such a method provides a flexible
dependence structure that allows for positive and negative correlation, as well. In addition …
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