Scholarlink

<p>In this article, inference under the balanced joint progressive type-II censoring scheme (BPTC) is studied when the lifetimes of two samples belong to a family of inverted exponentiated distributions with a common parameter. The study focuses on obtaining point and interval estimates for unknown parameters using classical and Bayesian approaches, considering cases where the common parameter is known and unknown. Existence and uniqueness of maximum likelihood estimators of parameters are established. We construct associated approximate confidence intervals as well. Bayes estimates are derived under unrestricted and restricted parameters cases by considering Beta-Gamma and gamma priors. To evaluate the performance of Bayes estimators, the study employs Monte Carlo simulations and compares the results with maximum likelihood estimates. Moreover, a real data set is analyzed in support of the BPTC model. Finally, we discuss optimal censoring plans under the considered scheme.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Order Restricted Inference for a Generalized Family of Inverted Exponentiated Distributions with BPTC Scheme

  • 期刊论文

  • 发表日期：

    2025年7月26日

查看全文
  • Kundan Singh,
  • Yogesh Mani Tripathi,
  • Chandrakant Lodhi,
  • Liang Wang

摘要

In this article, inference under the balanced joint progressive type-II censoring scheme (BPTC) is studied when the lifetimes of two samples belong to a family of inverted exponentiated distributions with a common parameter. The study focuses on obtaining point and interval estimates for unknown parameters using classical and Bayesian approaches, considering cases where the common parameter is known and unknown. Existence and uniqueness of maximum likelihood estimators of parameters are established. We construct associated approximate confidence intervals as well. Bayes estimates are derived under unrestricted and restricted parameters cases by considering Beta-Gamma and gamma priors. To evaluate the performance of Bayes estimators, the study employs Monte Carlo simulations and compares the results with maximum likelihood estimates. Moreover, a real data set is analyzed in support of the BPTC model. Finally, we discuss optimal censoring plans under the considered scheme.