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<p>In this article, we proposed a new exponentiated transformed Weibull (NETW) distribution, an extension of the Weibull distribution without introducing additional parameters. The aim of this article is discussed in three parts. Firstly, the characteristics of the NETW distribution have been discussed. Secondly, we have obtained the point and interval estimation of the model parameter under a progressive censoring scheme using classical and Bayesian approaches. We have used maximum likelihood and maximum product spacing estimation methods in the classical approach. Bayesian estimation has been obtained under informative gamma prior via likelihood and product spacing function. Moreover, we used the Monte Carlo simulation study to compare the results of all methods. In the third part of this article, we propose two criteria for finding the optimal censoring schemes using the variable neighbourhood search (VNS) algorithm. We obtained the single optimal design using each criterion and the compound optimal design using both criteria simultaneously. An application of the proposed methodology to a real-life data set of COVID-19 patients of Dominica has been discussed in order to demonstrate its applicability in practice. The proposed methods work consistently and effectively based on numerical results for real data.</p>

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Parameter Estimation and Optimal Plan Under Progressive Censoring for New Exponentiated Transformed Weibull Distribution

  • 期刊论文

  • 发表日期：

    2025年9月18日

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  • Aman Prakash,
  • Raj Kamal Maurya,
  • Okechukwu J. Obulezi

摘要

In this article, we proposed a new exponentiated transformed Weibull (NETW) distribution, an extension of the Weibull distribution without introducing additional parameters. The aim of this article is discussed in three parts. Firstly, the characteristics of the NETW distribution have been discussed. Secondly, we have obtained the point and interval estimation of the model parameter under a progressive censoring scheme using classical and Bayesian approaches. We have used maximum likelihood and maximum product spacing estimation methods in the classical approach. Bayesian estimation has been obtained under informative gamma prior via likelihood and product spacing function. Moreover, we used the Monte Carlo simulation study to compare the results of all methods. In the third part of this article, we propose two criteria for finding the optimal censoring schemes using the variable neighbourhood search (VNS) algorithm. We obtained the single optimal design using each criterion and the compound optimal design using both criteria simultaneously. An application of the proposed methodology to a real-life data set of COVID-19 patients of Dominica has been discussed in order to demonstrate its applicability in practice. The proposed methods work consistently and effectively based on numerical results for real data.