Inference for Kumaraswamy Distribution Based on Type I Progressive Hybrid Censoring

Farha Sultana, Yogesh Mani Tripathi (), Shuo-Jye Wu and Tanmay Sen
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Farha Sultana: Indian Institute of Technology Patna
Yogesh Mani Tripathi: Indian Institute of Technology Patna
Shuo-Jye Wu: Tamkang University
Tanmay Sen: Indian Institute of Technology Patna

Annals of Data Science, 2022, vol. 9, issue 6, No 9, 1283-1307

Abstract: Abstract 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.

Keywords: Bayes estimates; Importance sampling; Lindley approximation; Maximum likelihood estimates; One-sample prediction (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40745-020-00283-z

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