Open AccessThe Negative Binomial-New Generalized Lindley Distribution for Count Data: Properties and Application
Author(s) -
Sirinapa Aryuyuen
Publication year - 2022
Publication title -
pakistan journal of statistics and operation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 15
eISSN - 2220-5810
pISSN - 1816-2711
DOI - 10.18187/pjsor.v18i1.2988
Subject(s) - negative binomial distribution , count data , mathematics , negative multinomial distribution , statistics , poisson distribution , binomial distribution , compound poisson distribution , beta binomial distribution , estimator , exponential family , quasi likelihood , exponential distribution , poisson binomial distribution , gamma distribution , beta negative binomial distribution , poisson regression , population , demography , sociology
In this paper, a new mixture distribution for count data, namely the negative binomial-new generalized Lindley (NB-NGL) distribution is proposed. The NB-NGL distribution has four parameters, and is a flexible alternative for analyzing count data, especially when there is over-dispersion in the data. The proposed distribution has sub-models such as the negative binomial-Lindley (NB-L), negative binomial-gamma (NB-G), and negative binomial-exponential (NB-E) distributions as the special cases. Some properties of the proposed distribution are derived, i.e., the moments and order statistics density function. The unknown parameters of the NB-NGL distribution are estimated by using the maximum likelihood estimation. The results of the simulation study show that the maximum likelihood estimators give the parameter estimates close to the parameter when the sample is large. Application of NB-NGL distribution is carry out on three samples of medical data, industry data, and insurance data. Based on the results, it is shown that the proposed distribution provides a better fit compared to the Poisson, negative binomial, and its sub-model for count data.