Bivariate regression models based on compound Poisson distribution

Alzaid, Abdulhamid A.; Almuhayfith, Fatimah E.; Omair, Maha A.

doi:10.1080/03610926.2016.1152483

Cited by 2 publications

(1 citation statement)

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“…The estimated variance-covariance matrix of the maximum likelihood estimators are computed for each data set. In order to investigate the performance of the BGNBD, we compared the tting of this model with the results of tting the bivariate Poisson-Poisson (BPPD), bivariate binomial-Poisson (BBPD), bivariate geometric-Poisson (BGPD), and bivariate negative binomial-Poisson (BNBPD) distributions to the data (For more information about these distributions, see Alzaid et al (2017)). The BBPD is tted assuming dierent values of the parameter m, the BNBPD assuming dierent values of the parameter r for the rst two data sets, in this case the moments estimates coincide with the maximum likelihood estimates.…”

Section: Numerical Examplementioning

confidence: 99%

A Bivariate Model based on Compound Negative Binomial Distribution

Omair¹,

Almuhayfith²,

Alzaid³

2018

Rev. colomb. estad.

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A new bivariate model is introduced by compounding negative binomial and geometric distributions. Distributional properties, including joint, marginal and conditional distributions are discussed. Expressions for the product moments, covariance and correlation coecient are obtained. Some properties such as ordering, unimodality, monotonicity and self-decomposability are studied. Parameter estimators using the method of moments and maximum likelihood are derived. Applications to trac accidents data are illustrated.

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Section: Numerical Examplementioning

confidence: 99%