Statistical Inference of a New Pareto-type Model under Generalized Hybrid Type-I Censored Samples | ||
| Sohag Journal of Sciences | ||
| Volume 10, Issue 1, March 2025, Pages 10-23 PDF (465.54 K) | ||
| Document Type: Regular Articles | ||
| DOI: 10.21608/sjsci.2024.297521.1213 | ||
| Authors | ||
| Ahmed A. Soliman1; Gamal A Abd- Elmougod2; Alwageh Ahmed1; Osama Mohamed Taha* 1 | ||
| 1Department of Mathematics, Faculty of Science, Sohag University, Sohag 82524, Egypt. | ||
| 2Department of Mathematics, Faculty of Science, Damanhour University, Damanhour, 22511, Egypt | ||
| Abstract | ||
| In life-testing experiments, generalized hybrid Type-I censoring scheme (GHTCS) has been adopted to enhance the statistical efficiency of estimators. The core focus of this article is to extensively tackle the critical matter of estimation the model parameters and the parameters of life (reliability and hazard rate functions) for a new Pareto-type distribution (NPD) based on GHTCS. Initially, the model parameters as well as the parameters of life are estimated by maximum likelihood method and the corresponding approximate confidence intervals are constructed with respected to the observed Fisher information matrix. To address scenarios where sample sizes are small, confidence intervals are created by employing the percentile bootstrap method. In addition, the point and credible intervals estimate of parameters are constructed with respect to symmetric squared error loss Bayes method. To provide a robust and efficient framework for accurate estimation the approximate Bayes estimators are computed under the technique of Markov chain Monte Carlo (MCMC). The efficiency of estimators and comparative analysis of their performance are assessed under constructed the comprehensive simulation study. Ultimately, the application of the estimators is demonstrated through the analysis a set of real data. | ||
| Keywords | ||
| GHTCS; a new Pareto-type distribution; delta method; bootstrap method; Bayesian estimation; MCMC; importance sampling | ||
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