A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric Functions

In this paper, we provide a new bivariate distribution obtained from a Kibble-type bivariate gamma distribution. The stochastic representation was obtained by the sum of a Kibble-type bivariate random vector and a bivariate random vector builded by two independent gamma random variables. In addition...

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Main Authors: Christian Caamaño-Carrillo, Javier E. Contreras-Reyes
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/10/9/1502
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author Christian Caamaño-Carrillo
Javier E. Contreras-Reyes
author_facet Christian Caamaño-Carrillo
Javier E. Contreras-Reyes
author_sort Christian Caamaño-Carrillo
collection DOAJ
description In this paper, we provide a new bivariate distribution obtained from a Kibble-type bivariate gamma distribution. The stochastic representation was obtained by the sum of a Kibble-type bivariate random vector and a bivariate random vector builded by two independent gamma random variables. In addition, the resulting bivariate density considers an infinite series of products of two confluent hypergeometric functions. In particular, we derive the probability and cumulative distribution functions, the moment generation and characteristic functions, the Hazard, Bonferroni and Lorenz functions, and an approximation for the differential entropy and mutual information index. Numerical examples showed the behavior of exact and approximated expressions.
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spelling doaj.art-664d48ac20b44e31872f21594403030a2023-11-23T08:45:19ZengMDPI AGMathematics2227-73902022-05-01109150210.3390/math10091502A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric FunctionsChristian Caamaño-Carrillo0Javier E. Contreras-Reyes1Departamento de Estadística, Facultad de Ciencias, Universidad del Bío-Bío, Concepción 4081112, ChileInstituto de Estadística, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso 2360102, ChileIn this paper, we provide a new bivariate distribution obtained from a Kibble-type bivariate gamma distribution. The stochastic representation was obtained by the sum of a Kibble-type bivariate random vector and a bivariate random vector builded by two independent gamma random variables. In addition, the resulting bivariate density considers an infinite series of products of two confluent hypergeometric functions. In particular, we derive the probability and cumulative distribution functions, the moment generation and characteristic functions, the Hazard, Bonferroni and Lorenz functions, and an approximation for the differential entropy and mutual information index. Numerical examples showed the behavior of exact and approximated expressions.https://www.mdpi.com/2227-7390/10/9/1502generalized gamma distributiongeneralized hypergeometric functionmoment generation functiondifferential entropymutual information
spellingShingle Christian Caamaño-Carrillo
Javier E. Contreras-Reyes
A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric Functions
Mathematics
generalized gamma distribution
generalized hypergeometric function
moment generation function
differential entropy
mutual information
title A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric Functions
title_full A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric Functions
title_fullStr A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric Functions
title_full_unstemmed A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric Functions
title_short A Generalization of the Bivariate Gamma Distribution Based on Generalized Hypergeometric Functions
title_sort generalization of the bivariate gamma distribution based on generalized hypergeometric functions
topic generalized gamma distribution
generalized hypergeometric function
moment generation function
differential entropy
mutual information
url https://www.mdpi.com/2227-7390/10/9/1502
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