文章基本信息

标题：THE EXPONENTIATED GENERALIZED EXTENDED GOMPERTZ DISTRIBUTION
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作者：Thiago A. N. De Andrade ; Subrata Chakraborty ; Laba Handique 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2019
卷号：17
期号：2
页码：299-330
DOI：10.6339/JDS.201904_17(2).0004
出版社：Tingmao Publish Company
摘要：This paper presents a new generalization of the extended Gompertz distribution. We defined the so-called exponentiated generalized extended Gompertz distribution, which has at least three important advantages: (i) Includes the exponential, Gompertz, extended exponential and extended Gompertz distributions as special cases; (ii) adds two parameters to the base distribution, but does not use any complicated functions to that end; and (iii) its hazard function includes inverted bathtub and bathtub shapes, which are particularly important because of its broad applicability in real-life situations. The work derives several mathematical properties for the new model and discusses a maximum likelihood estimation method. For the main formulas related to our model, we present numerical studies that demonstrate the practicality of computational implementation using statistical software. We also present a Monte Carlo simulation study to evaluate the performance of the maximum likelihood estimators for the EGEG model. Three real- world data sets were used for applications in order to illustrate the usefulness of our proposal..
关键词：Applied results; exponentiated generalized class; Gompertz