Bivariate Poisson-Lindley distribution with application

Hossein Zamani, Pouya Faroughi, Noriszura Ismail

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This study applies a Bivariate Poisson-Lindley (BPL) distribution for modeling dependent and over-dispersed count data. The advantage of using this form of BPL distribution is that the correlation coefficient can be positive, zero or negative, depending on the multiplicative factor parameter. Several properties such as mean, variance and correlation coefficient of the BPL distribution are discussed. A numerical example is given and the BPL distribution is compared to Bivariate Poisson (BP) and Bivariate Negative Binomial (BNB) distributions which also allow the correlation coefficient to be positive, zero or negative. The results show that BPL distribution provides the smallest Akaike Information Criterion (AIC), indicating that the distribution can be used as an alternative for fitting dependent and over-dispersed count data, with either negative or positive correlation.

Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalJournal of Mathematics and Statistics
Volume11
Issue number1
DOIs
Publication statusPublished - 2015

Fingerprint

Bivariate Distribution
Poisson distribution
Correlation coefficient
Count Data
Negative binomial distribution
Akaike Information Criterion
Dependent
Zero
Multiplicative
Siméon Denis Poisson
Numerical Examples
Alternatives
Modeling

Keywords

  • Bivariate
  • Count data
  • Dependent
  • Over-dispersed
  • Poisson-Lindley

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

Bivariate Poisson-Lindley distribution with application. / Zamani, Hossein; Faroughi, Pouya; Ismail, Noriszura.

In: Journal of Mathematics and Statistics, Vol. 11, No. 1, 2015, p. 1-6.

Research output: Contribution to journalArticle

Zamani, Hossein ; Faroughi, Pouya ; Ismail, Noriszura. / Bivariate Poisson-Lindley distribution with application. In: Journal of Mathematics and Statistics. 2015 ; Vol. 11, No. 1. pp. 1-6.
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