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

Nada M AlfaerSarah A BandarOmid KharazmiHazem Al-MoflehZubair AhmadAhmed Z Afify
Published in: PloS one (2023)
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.
Keyphrases
  • electronic health record
  • monte carlo
  • big data
  • health insurance
  • healthcare
  • machine learning