Zero-Dependent Bivariate Poisson Distribution with Applications

Najla Qarmalah () and Abdulhamid A. Alzaid
Additional contact information
Najla Qarmalah: Department of Mathematical Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
Abdulhamid A. Alzaid: Department of Statistics and Operations Research, King Saud University, Riyadh 145111, Saudi Arabia

Mathematics, 2023, vol. 11, issue 5, 1-16

Abstract: The bivariate Poisson model is the most widely used model for bivariate counts, and in recent years, several bivariate Poisson regression models have been developed in order to analyse two response variables that are possibly correlated. In this paper, a particular class of bivariate Poisson model, developed from the bivariate Bernoulli model, will be presented and investigated. The proposed bivariate Poisson models use dependence parameters that can model positively and negatively correlated data, whereas more well-known models, such as Holgate’s bivariate Poisson model, can only be used for positively correlated data. As a result, the proposed model contributes to improving the properties of the more common bivariate Poisson regression models. Furthermore, some of the properties of the new bivariate Poisson model are outlined. The method of maximum likelihood and moment method were used to estimate the parameters of the proposed model. Additionally, real data from the healthcare utilization sector were used. As in the case of healthcare utilization, dependence between the two variables may be positive or negative in order to assess the performance of the proposed model, in comparison to traditional bivariate count models. All computations and graphs shown in this paper were produced using R programming language.

Keywords: Poisson; Bernoulli; count data; maximum likelihood; moment method; regression; bivariate models (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/5/1194/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/5/1194/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:5:p:1194-:d:1083707

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().