Approximate ML Estimation of Three-Parameter Exponentiated Weibull Distribution under Type-II Censoring

Authors

  • Naveen Gupta Jalluri Department of Statistics, Acharya Nagarjuna University, Guntur-522510, Andhra Pradesh, India
  • S. Bhanu Prakash Freshmen Engineering Department, Aditya University, Surampalem-533437, Andhra Pradesh, India
  • Syed Jilani Department of Statistics, Acharya Nagarjuna University, Guntur-522510, Andhra Pradesh, India
  • A. Vasudeva Rao Department of Statistics, Acharya Nagarjuna University, Guntur-522510, Andhra Pradesh, India

DOI:

https://doi.org/10.19139/soic-2310-5070-3098

Keywords:

Exponentiated Weibull distribution, M L estimator, approximate M L estimator, percentile estimator, Type-II censored sample, interval estimation.

Abstract

In this paper, we propose an approximate maximum likelihood estimator for the three parameter exponentiated Weibull Distribution (EWD) under Type-II censoring. We also derive a percentile estimator of the scale parameter based on a Type-II censored sample. We compare both AMLE and PCE with the MLE of the scale parameter based on bias and mean square error (MSE.) through a Monte Carlo simulation study, we found that AMLE is as efficient as MLE in terms of both bias and MSE. In addition, interval estimation based on MLE and AMLE for the scale parameter is also discussed. To find more accurate values for the shape parameters, we propose a step-by-step method. This process combines a basic linear estimation technique with the standard Maximum Likelihood (ML) equation. By solving these together repeatedly, we can produce new, more precise results known as Approximate Maximum Likelihood Estimators (AMLEs). Finally, we present two data sets that include one real data set to emphasize the results of this article.

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Published

2026-10-05

How to Cite

Jalluri, N. G., Prakash, S. B. ., Jilani, S. ., & Rao, A. V. . (2026). Approximate ML Estimation of Three-Parameter Exponentiated Weibull Distribution under Type-II Censoring. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-3098

Issue

Online First

Section

Research Articles

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Copyright (c) 2026 Naveen Gupta Jalluri, S. Bhanu Prakash, Syed Jilani, A. Vasudeva Rao

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