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Applications of Beta Negative Binomial Distribution Series on Holomorphic Functions

, 2021
The purpose of this article is to derive the necessary and sufficient conditions for the power series ℘ג, whose coefficients are probabilities of the beta negative binomial distribution to be in the family F , , , , , ג, of holomorphic functions which ...
A. Wanas, Najah Ali Jiben Al-Ziadi
semanticscholar   +1 more source

Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

Transportmetrica A: Transport Science, 2022
Several studies have reported the superior performance of the Negative Binomial–Lindley (NB-L) compared to the commonly used Negative Binomial distribution.
A. Khodadadi   +3 more
semanticscholar   +1 more source

Generalized Negative Binomial Distributions [PDF]

open access: possibleJournal of Statistical Physics, 2000
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

THE TRUNCATED NEGATIVE BINOMIAL DISTRIBUTION [PDF]

open access: possibleBiometrika, 1955
(1920), Fisher (1941), Haldane (1941), Anscoinbe (1950) and Bliss & Fisher (1953), and is extensively used for the description of data too heterogeneous to be fitted by a Poisson distribution. Observed samples, however, may be truncated, in the sense that the number of individuals falling into the zero class cannot be determined.
openaire   +1 more source

The Negative Binomial Distribution

The Statistician, 1985
This note sketches a biological context for the negative binomial distribu- tion, gives some of the many equivalent mathematical notations that have been used for the negative binomial probabilities, and discusses the use of the computer program MLP (the Maximum Likelihood Program) for fitting negative binomial distributions to data.
G. J. S. Ross, D. A. Preece
openaire   +2 more sources

A Generalized Negative Binomial Distribution

SIAM Journal on Applied Mathematics, 1971
A generalized negative binomial (GNB) distribution with an additional parameter $\beta $ has been obtained by using Lagrange’s expansion. The parameter is such that both mean and variance tend to increase or decrease with an increase or decrease in its value but the variance increases or decreases faster than the mean.
G. C. Jain, P.C. Consul
openaire   +3 more sources

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