Pivotal Inference for the Generalized Logistic Distribution Based on Progressively Type-II Censored Samples

被引：0

作者：

Seo, Yeon-Ju ^{[1
]}

Kang, Suk-Bok ^{[1
]}

Seo, Jung-In ^{[1
]}

机构：

[1] Yeungnam Univ, Dept Stat, 280 Daehak Ro, Gyongsan, South Korea

来源：

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS | 2015年 / 53卷 / 03期

关键词：

Generalized Logistic Distribution; Pivotal Quantity; Progressively Type-II Censoring Schemes;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper addresses estimation problems for unknown parameters of the generalized logistic distribution based on progressively Type-II censored samples. The maximum likelihood estimation method is the most popular method for estimating unknown parameters of probability distributions. However, it can entail a significant bias if the distribution is skewed or the sample is censored. To overcome this disadvantage, the paper proposes estimation methods based on pivotal quantities and compares them with the maximum likelihood estimation method through Monte Carlo simulations for various progressively Type-II censoring schemes.

引用

页码：120 / 127

页数：8

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