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Bivariate exponentiated‐exponential geometric regression model

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  • Felix Famoye

Abstract

A bivariate exponentiated‐exponential geometric regression model that allows negative, zero, or positive correlation is defined and studied. The model can accommodate under‐ or over‐dispersed count data. The regression model is based on the univariate exponentiated‐exponential geometric distribution, and the marginal means of the bivariate model are functions of the explanatory variables. The parameters of the bivariate regression model are estimated by using the maximum likelihood method. Some test statistics including goodness of fit are discussed. A simulation study is conducted to compare the model with the bivariate generalized Poisson regression model. One numerical data set is used to illustrate the application of the regression model.

Suggested Citation

  • Felix Famoye, 2019. "Bivariate exponentiated‐exponential geometric regression model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 73(3), pages 434-450, August.
    • Handle: RePEc:bla:stanee:v:73:y:2019:i:3:p:434-450
    DOI: 10.1111/stan.12177
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    Cited by:

    1. Lluís Bermúdez & Dimitris Karlis, 2021. "Multivariate INAR(1) Regression Models Based on the Sarmanov Distribution," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
    2. Agu Friday Ikechukwu & Eghwerido Joseph Thomas, 2021. "Agu-Eghwerido distribution, regression model and applications," Statistics in Transition New Series, Polish Statistical Association, vol. 22(4), pages 59-76, December.
    3. Friday Ikechukwu Agu & Joseph Thomas Eghwerido, 2021. "Agu-Eghwerido distribution, regression model and applications," Statistics in Transition New Series, Polish Statistical Association, vol. 22(4), pages 59-76, December.

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