Statistical Inference for Competing Risks Model with Adaptive Progressively Type-II Censored Gompertz Life Data Using Industrial and Medical Applications

Muqrin A. Almuqrin (m.almuqrin@mu.edu.sa), Mukhtar M. Salah and Essam A. Ahmed (emohammed@taibahu.edu.sa)
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Muqrin A. Almuqrin: Department of Mathematics, Faculty of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Mukhtar M. Salah: Department of Mathematics, Faculty of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia
Essam A. Ahmed: Faculty of Business Administration, Taibah University, Medina 42353, Saudi Arabia

Mathematics, 2022, vol. 10, issue 22, 1-38

Abstract: This study uses the adaptive Type-II progressively censored competing risks model to estimate the unknown parameters and the survival function of the Gompertz distribution. Where the lifetime for each failure is considered independent, and each follows a unique Gompertz distribution with different shape parameters. First, the Newton-Raphson method is used to derive the maximum likelihood estimators (MLEs), and the existence and uniqueness of the estimators are also demonstrated. We used the stochastic expectation maximization (SEM) method to construct MLEs for unknown parameters, which simplified and facilitated computation. Based on the asymptotic normality of the MLEs and SEM methods, we create the corresponding confidence intervals for unknown parameters, and the delta approach is utilized to obtain the interval estimation of the reliability function. Additionally, using two bootstrap techniques, the approximative interval estimators for all unknowns are created. Furthermore, we computed the Bayes estimates of unknown parameters as well as the survival function using the Markov chain Monte Carlo (MCMC) method in the presence of square error and LINEX loss functions. Finally, we look into two real data sets and create a simulation study to evaluate the efficacy of the established approaches.

Keywords: Gompertz distribution; competing risks model; adaptive progressively Type-II censoring; maximum likelihood estimation; stochastic EM algorithm; bootstrap methods; delta method; Bayes estimator; Markov chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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