A New Bivariate Distribution With Applications on Dependent Competing Risks Data

Thamer Manshi, A. Sarhan, Bruce Smith
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Abstract

A new bivariate distribution is proposed in this paper using the univariate modified Weibull extension distribution. The proposed distribution is referred to as the bivariate Modified Weibull Extension (BMWE) distribution. The BMWE distribution is of Marshall-Olkin type. We discuss some of the statistical properties of the BMWE  distribution.  Applications of this distribution to dependent competing risks data are discussed.  The maximum likelihood estimators (MLE) of the model parameters using both bivariate data and dependent competing risks data are discussed. These MLE's cannot be obtained in closed form. Therefore, numerical optimization methods are  applied. A simulation study is carried out to investigate the performance of the estimation technique. Two real data sets;  one bivariate data set and another dependent competing risks data set, are analyzed using the proposed distribution for illustrative and comparison purposes.
一种新的二元分布及其在相依竞争风险数据中的应用
利用单变量修正威布尔扩展分布,提出了一种新的二元分布。所提出的分布称为二元修正威布尔扩展(BMWE)分布。BMWE分布为Marshall-Olkin型。我们讨论了BMWE分布的一些统计性质。讨论了该分布在相互依赖的竞争风险数据中的应用。讨论了二元数据和相关竞争风险数据下模型参数的极大似然估计。这些MLE不能以封闭形式获得。因此,采用数值优化方法。仿真研究了该估计技术的性能。两个真实数据集;一个双变量数据集和另一个依赖的竞争风险数据集,使用提出的分布进行分析,以说明和比较的目的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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