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Review of univariate and bivariate exponentiated exponential‐geometric distributions

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Abstract The exponentiated exponential (or generalized exponential) distribution is a two‐parameter right skewed unimodal density function. A discrete analogue of the distribution is often called the exponentiated exponential‐geometric (or discrete generalized exponential) distribution. We review some methods for generating discrete analogues of the exponentiated exponential distribution and discuss some of their properties. Among the important properties of the univariate distribution are (a) closed form cumulative distribution function and probability mass function and (b) ability to accommodate under‐, equi‐, or over‐dispersion. Some methods to extend the univariate distribution to the bivariate exponentiated exponential‐geometric distribution are discussed. The extension of univariate and bivariate distributions to count data regression models is briefly described. It is expected that this review will serve as a reference and encourage further research work and generalizations of the exponentiated exponential‐geometric distribution. This article is categorized under: Statistical Models > Linear Models Data: Types and Structure > Categorical Data Statistical Models > Generalized Linear Models

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Statistical Models > Generalized Linear Models
Data: Types and Structure > Categorical Data
Statistical Models > Linear Models

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