Statistical Inference of a New Pareto-type Model under Generalized Hybrid Type-I Censored Samples

Document Type : Regular Articles

Authors

1 Department of Mathematics, Faculty of Science, Sohag University, Sohag 82524, Egypt.

2 Department of Mathematics, Faculty of Science, Damanhour University, Damanhour, 22511, Egypt

10.21608/sjsci.2024.297521.1213

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.

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