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July 21, 2026Mathematics0 citationsOpen Access

Adaptive Type-II Competing Risks Analysis for the Generalized Inverted Exponential Model Using Medical Applications

SAShuhrah AlghamdiPrincess Nourah bint Abdulrahman University RSRaga ShiekhUniversity of Tabuk GIGamal M. IsmailIslamic University of Madinah

Key Points

This research aims to explore parameter estimation under an adaptive progressive Type-II competing risks framework for the Generalized Inverted Exponential model.
Developed a competing risks model assuming two independent failure causes following a GIE distribution.
Utilized maximum likelihood estimation and Bayesian methods with Markov Chain Monte Carlo simulation for credible intervals.
Applied methodologies to real datasets and conducted a Monte Carlo simulation study to evaluate performance.
Demonstrated effectiveness of the estimation methods through real data applications.
Provided statistical insights into the performance of parameter estimates in the GIE model with constructed confidence intervals.

Abstract

This study investigates parameter estimation utilizing an adaptive progressive Type-II competing risks framework. In this work, we provide a comprehensive analysis of the statistical features and estimation methods for the Generalized Inverted Exponential (GIE) model. Assuming a population subject to two independent failure causes, both following a GIE distribution, a competing risks model is formulated. Point and interval estimates are derived using both maximum likelihood, with associated asymptotic confidence intervals, and Bayesian approaches via Markov Chain Monte Carlo simulation to obtain credible intervals. The effectiveness of these methods is subsequently demonstrated by applying them to real datasets and through a comprehensive Monte Carlo simulation study designed to assess estimator performance.

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