Generalized Poisson–Lindley linear model for count data. Issue 15 (18th November 2017)

• Record Type: Journal Article
• Authors: Wongrin, Weerinrada; Bodhisuwan, Winai
• Is Part Of: Journal of applied statistics. Volume 44:Issue 15(2017)
• Page Start: 2659
• Page End: 2671
• Publication Date: 2017-11-18
• Subjects: Count data -- generalized Poisson–Lindley distribution -- generalized linear model -- maximum likelihood estimation -- over-dispersion; 62J12; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1260095
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: The purpose of this paper is to develop a new linear regression model for count data, namely generalized-Poisson Lindley (GPL) linear model. The GPL linear model is performed by applying generalized linear model to GPL distribution. The model parameters are estimated by the maximum likelihood estimation. We utilize the GPL linear model to fit two real data sets and compare it with the Poisson, negative binomial (NB) and Poisson-weighted exponential (P-WE) models for count data. It is found that the GPL linear model can fit over-dispersed count data, and it shows the highest log-likelihood, the smallest AIC and BIC values. As a consequence, the linear regression model from the GPL distribution is a valuable alternative model to the Poisson, NB, and P-WE models.