Multivariate count data arise in many fields of applied sciences and modeling such data is a relevant task. Here we consider the construction of a bivariate model with discrete Weibull margins, based on the Farlie-Gumbel-Morgenstern copula, allowing for a slight level of (positive/negative) correlation, and propose several methods for the point estimation of its parameters. For illustrative purposes, the model and related inferential procedures are fitted and applied to a dataset taken from the literature.
Discrete Weibull variables linked by Farlie-Gumbel-Morgenstern copula / A. Barbiero (AIP CONFERENCE PROCEEDINGS). - In: Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2016 (ICNAAM-2016)[s.l] : AIP, 2017 Jul 21. - ISBN 9780735415386. - pp. 1-4 (( Intervento presentato al 14. convegno International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2016) tenutosi a Rhodes nel 2016 [10.1063/1.4992408].
Discrete Weibull variables linked by Farlie-Gumbel-Morgenstern copula
A. Barbiero
2017
Abstract
Multivariate count data arise in many fields of applied sciences and modeling such data is a relevant task. Here we consider the construction of a bivariate model with discrete Weibull margins, based on the Farlie-Gumbel-Morgenstern copula, allowing for a slight level of (positive/negative) correlation, and propose several methods for the point estimation of its parameters. For illustrative purposes, the model and related inferential procedures are fitted and applied to a dataset taken from the literature.
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