Statistical Inference for the Inverted Kumaraswamy Accelerated Model Under Type-I Generalized Hybrid Censoring with Applications

Symmetry 2026 Open access

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Authors: Gamal Mohamed Ismail, Ohud A. Alqasem, Lamis M. Alamoudi, Maryam Ibrahim Habadi, Meshayil M. Alsolmi, Raga Hassan Ali Shiekh, Md. Mahabubur Rahman, Samah M. Ahmed
DOI: 10.3390/sym18030446
Cited by: 1
References: 29

Abstract

This study investigates the methodologies for robust parameter estimation within the context of the parameters of the inverted Kumaraswamy model using data derived from step-stress partially accelerated life testing with Type-I generalized hybrid censoring. We formulate estimation procedures within both frequentist (maximum likelihood) and Bayesian frameworks, including the construction of asymptotic and credible intervals. Subsequently, we provide a formal derivation of the associated asymptotic and bootstrap confidence intervals. To address the analytical intractability of the Bayesian estimation, we employ Markov Chain Monte Carlo techniques. The proposed methods are illustrated through an illustrative example, an application to real-world precipitation data, and a simulation study.

Fields of Study & Subjects

Statistical Distribution Estimation and Applications Statistical Methods and Inference Statistical Methods and Bayesian Inference

Publication details

Journal / source
Symmetry
Publisher
MDPI AG
Volume
18
Issue
3
Pages
446–446
Publication date
4 March 2026
Language
EN