Classical and Bayesian estimation for type-I extended-F family with an actuarial application.

Autor: Alfaer NM; Department of Mathematics & Statistics, College of Science, Taif University, Taif, Saudi Arabia., Bandar SA; Department of Mathematics, College of Education, Misan University, Amarah, Iraq., Kharazmi O; Department of Statistics, Faculty of Sciences, Vali-e-Asr University of Rafsanjan, Rafsanjan, Pakistan., Al-Mofleh H; Department of Mathematics, Tafila Technical University, Tafila, Jordan., Ahmad Z; Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan., Afify AZ; Department of Statistics, Mathematics and Insurance, Benha University, Benha, Egypt.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Feb 02; Vol. 18 (2), pp. e0275430. Date of Electronic Publication: 2023 Feb 02 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0275430
Abstrakt: In this work, a new flexible class, called the type-I extended-F family, is proposed. A special sub-model of the proposed class, called type-I extended-Weibull (TIEx-W) distribution, is explored in detail. Basic properties of the TIEx-W distribution are provided. The parameters of the TIEx-W distribution are obtained by eight classical methods of estimation. The performance of these estimators is explored using Monte Carlo simulation results for small and large samples. Besides, the Bayesian estimation of the model parameters under different loss functions for the real data set is also provided. The importance and flexibility of the TIEx-W model are illustrated by analyzing an insurance data. The real-life insurance data illustrates that the TIEx-W distribution provides better fit as compared to competing models such as Lindley-Weibull, exponentiated Weibull, Kumaraswamy-Weibull, α logarithmic transformed Weibull, and beta Weibull distributions, among others.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Alfaer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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