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arXiv 1902.05658stat.CO
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韦布尔分布参数的一种新估计--韦布尔分布的综合比较研究

A new estimator for Weibull distribution parameters: Comprehensive comparative study for Weibull Distribution

Sahar Sadani, Kamel Abdollahnezhad, Mahdi Teimouri, Vahid Ranjbar

摘要:

韦布尔分布在工程和科学中得到了广泛的应用。估计量的效用和有用性在很大程度上取决于从业者的研究领域。在实际应用中,用户需要在不同的参数设置和样本量下寻找自己想要的估计量。在本文中,我们集中在两个主题。首先,我们提出了韦布尔分布参数的$U$-统计量。从理论上和模拟上证明了引入的$U$-统计量的相合性和渐近正态性。文献中提出了几种估计韦布尔分布参数的方法。这些方法包括:广义最小二乘类型1、广义最小二乘类型2、$L$-矩、对数矩、极大似然估计、矩方法、百分位数方法、加权最小二乘和加权极大似然估计。其次,由于缺乏对韦布尔分布参数估计量之间的全面比较,本文将我们提出的$U$-统计量与上述九种估计量进行了全面的比较研究。基于模拟,事实证明,我们提出的$U$-统计量显示出最好的性能,估计的形状和尺度参数时,样本量大的偏差。

英文摘要:

Weibull distribution has received a wide range of applications in engineering and science. The utility and usefulness of an estimator is highly subject to the field of practitioner's study. In practice users looking for their desired estimator under different setting of parameters and sample sizes. In this paper we focus on two topics. Firstly, we propose $U$-statistics for the Weibull distribution parameters. The consistency and asymptotically normality of the introduced $U$-statistics are proved theoretically and by simulations. Several of methods have been proposed for estimating the parameters of Weibull distribution in the literature. These methods include: the generalized least square type 1, the generalized least square type 2, the $L$-moments, the Logarithmic moments, the maximum likelihood estimation, the method of moments, the percentile method, the weighted least square, and weighted maximum likelihood estimation. Secondary, due to lack of a comprehensive comparison between the Weibull distribution parameters estimators, a comprehensive comparison study is made between our proposed $U$-statistics and above nine estimators. Based on simulations, it turns out that the our proposed $U$-statistics show the best performance in terms of bias for estimating the shape and scale parameters when the sample size is large.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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