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Abstract
We introduce a new family of continuous distributions and study the mathematical properties of the new family. Some useful characterizations based on the ratio of two truncated moments and hazard function are also presented. We estimate the model parameters by the maximum likelihood method and assess its performance based on biases and mean squared errors in a simulation study framework.
Highlights
Several continuous univariate models have been widely used for modeling real data sets in many areas such as life sciences, engineering, economics, biological studies and environmental sciences to name a few
We present a new class of distributions called the Type I General Exponential (TIGE) family of distributions, The mathematical properties of this new family including explicit expansions for the ordinary and incomplete moments, generating function, mean deviations, order statistics, probability weighted moments are provided
Characterizations based on two truncated moments are presented
Summary
Several continuous univariate models have been widely used for modeling real data sets in many areas such as life sciences, engineering, economics, biological studies and environmental sciences to name a few. Various families of distributions have been constructed by extending common families of continuous distributions. These generalized distributions give more flexibility by adding one "or more" parameters to the baseline model. Gupta et al (1998) proposed the exponentiated-G class, which consists of raising the cumulative distribution function (cdf) to a positive power parameter.
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