Inference for Burr XII distribution under Type I progressive hybrid censoring

被引：12

作者：

Kayal, T. ^{[1
]}

Tripathi, Yogesh Mani ^{[1
]}

Rastogi, M. K. ^{[2
]}

Asgharzadeh, A. ^{[3
]}

机构：

[1] Indian Inst Technol Patna, Dept Math, Patna 801103, Bihar, India

[2] Natl Inst Pharmaceut Educ & Res, Hajipur, Bihar, India

[3] Univ Mazandaran, Dept Stat, Babol Sar, Iran

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2017年 / 46卷 / 09期

关键词：

Lindley approximation; Maximum likelihood estimates; Metropolis-Hastings algorithm; Prediction intervals; Predictive estimates; Type I progressive hybrid censoring; STATISTICAL-INFERENCE; WEIBULL DISTRIBUTION; PARAMETERS; MODEL;

D O I：

10.1080/03610918.2016.1241405

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：

We consider estimation of unknown parameters of a Burr XII distribution based on progressively Type I hybrid censored data. The maximum likelihood estimates are obtained using an expectation maximization algorithm. Asymptotic interval estimates are constructed from the Fisher information matrix. We obtain Bayes estimates under the squared error loss function using the Lindley method and Metropolis-Hastings algorithm. The predictive estimates of censored observations are obtained and the corresponding prediction intervals are also constructed. We compare the performance of the different methods using simulations. Two real datasets have been analyzed for illustrative purposes.

引用

页码：7447 / 7465

页数：19

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