文章基本信息

标题：Analysis of Count Data by Transmuted Geometric Distribution
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作者：Subrata Chakraborty ; Deepesh Bhati
期刊名称：Journal of Statistical Theory and Applications (JSTA)
电子版ISSN：1538-7887
出版年度：2019
卷号：18
期号：4
页码：450-463
DOI：10.2991/jsta.d.191218.001
语种：English
出版社：Atlantis Press
摘要：Transmuted geometric distribution (TGD) was recently introduced and investigated by Chakraborty and Bhati [Stat. Oper. Res. Trans. 40 (2016), 153–176]. This is a flexible extension of geometric distribution having an additional parameter that determines its zero inflation as well as the tail length. In the present article we further study this distribution for some of its reliability, stochastic ordering and parameter estimation properties. In parameter estimation among others we discuss an EM algorithm and the performance of estimators is evaluated through extensive simulation. For assessing the statistical significance of additional parameter α, Likelihood ratio test, the Rao's score tests and the Wald's test are developed and its empirical power via simulation are compared. We have demonstrate two applications of (TGD) in modeling real life count data.
关键词：Transmuted geometric distribution; EM algorithm; Likelihood Ratio test; Rao score's test; Wald's test