Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring

被引：0

作者：

Sultana F. ^{[1
]}

Tripathi Y.M. ^{[1
]}

Wu S.-J. ^{[2
]}

Sen T. ^{[1
]}

机构：

[1] Department of Mathematics, Indian Institute of Technology Patna, Patna

[2] Department of Statistics, Tamkang University, Tamsui, New Taipei City

来源：

Annals of Data Science | 2022年 / 9卷 / 06期

关键词：

Bayes estimates; Importance sampling; Lindley approximation; Maximum likelihood estimates; One-sample prediction;

D O I：

10.1007/s40745-020-00283-z

中图分类号：

学科分类号：

摘要：

In this paper, we investigate the estimation problems of unknown parameters of the Kumaraswamy distribution under type I progressive hybrid censoring. This censoring scheme is a combination of progressive type I and hybrid censoring schemes. We derive the maximum likelihood estimates of parameters using an expectation-maximization algorithm. Bayes estimates are obtained under different loss functions using the Lindley method and importance sampling procedure. The highest posterior density intervals of unknown parameters are constructed as well. We also obtain prediction estimates and prediction intervals for censored observations. A Monte Carlo simulation study is performed to compare proposed methods and one real data set is analyzed for illustrative purposes. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

引用

页码：1283 / 1307

页数：24

共 43 条

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