Inference of R = P(Y < X) for two-parameter Rayleigh distribution based on progressively censored samples

被引：7

作者：

Kohansal, Akram ^{[1
]}

Rezakhah, Saeid ^{[2
]}

机构：

[1] Imam Khomeini Int Univ, Dept Stat, Qazvin, Iran

[2] Amirkabir Univ Technol, Fac Math & Comp Sci, Tehran, Iran

来源：

STATISTICS | 2019年 / 53卷 / 01期

关键词：

Bayesian estimator; confidence interval; maximum likelihood estimator; Monte Carlo simulation; progressive Type-II censoring; P(Y-LESS-THAN-X); RELIABILITY;

D O I：

10.1080/02331888.2018.1546306

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The UMVUE and maximum likelihood estimator of R = P(Y < X) for independent progressively Type-II censored samples from two-parameter Rayleigh distributions with different scale parameters are derived. Also the exact, asymptotic and bootstrap confidence intervals for R are evaluated. Using Gibbs sampling, the Bayes estimates and corresponding credible intervals for R are obtained. Applying Monte Carlo simulations, we compare the performances of the different estimation methods. Finally we use of two real data sets and show the competitive performance of the presented methods.

引用

页码：81 / 100

页数：20

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Inference of R = P(Y &lt; X) for two-parameter Rayleigh distribution based on progressively censored samples

Inference of R = P(Y < X) for two-parameter Rayleigh distribution based on progressively censored samples