A Closed Form Approximation of Moments of New Generalization of Negative Binomial Distribution

In this paper, we propose a closed form approximation to the mean and variance of a new generalization of negative binomial (NGNB) distribution arising from the Extended COM-Poisson (ECOMP) distribution developed by Chakraborty and Imoto (2016)(see [4]). The NGNB is a special case of the ECOMP distr...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Roy, Sudip, Tripathi, Ram C, Balakrishnan, N
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.04.2019
Subjects
Approximation
Binomial distribution
Closed form solutions
Constraining
Convolution
Exact solutions
Mathematical analysis
Random variables
Regression models
Online AccessGet full text

Cover

More Information
Summary:In this paper, we propose a closed form approximation to the mean and variance of a new generalization of negative binomial (NGNB) distribution arising from the Extended COM-Poisson (ECOMP) distribution developed by Chakraborty and Imoto (2016)(see [4]). The NGNB is a special case of the ECOMP distribution and was named so by these authors. This distribution is more flexible in terms of the dispersion index as compared to its ordinary counterparts. It approaches the COM-Poisson distribution (Shmueli et al. 2005) [11] under suitable limiting conditions. The NGNB can also be obtained from the COM-Negative Hypergeometric distribution (Roy et al. 2019)[10] as a limiting distribution. In this paper, we present closed-form approximations for the mean and variance of the NGNB distribution. These approximations can be viewed as the mean and variance of convolution of independent and identically distributed negative binomial populations. The proposed closed-form approximations of the mean and variance will be helpful in building the link function for the generalized negative binomial regression model based on the NGNB distribution and other extended applications, hence resulting in enhanced applicability of this model.
ISSN:2331-8422