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Activity Number: 61
Type: Topic Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Government Statistics Section
Abstract #318980
Title: Bivariate Exponentiated-Exponential Geometric Regression Model
Author(s): Felix Famoye*
Companies: Central Michigan University
Keywords: Correlated count data ; dispersion ; estimation ; goodness-of-fit
Abstract:

A bivariate exponentiated-exponential geometric regression (BEEGR) model that allows any type of correlation is defined and studied. 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. One numerical data set is used to illustrate the applications of the regression model.


Authors who are presenting talks have a * after their name.

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