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  • Open Access


    Inference on Generalized Inverse-Pareto Distribution under Complete and Censored Samples

    Abdelaziz Alsubie1, Mostafa Abdelhamid2, Abdul Hadi N. Ahmed2, Mohammed Alqawba3, Ahmed Z. Afify4,*

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 213-232, 2021, DOI:10.32604/iasc.2021.018111

    Abstract In this paper, the estimation of the parameters of extended Marshall-Olkin inverse-Pareto (EMOIP) distribution is studied under complete and censored samples. Five classical methods of estimation are adopted to estimate the parameters of the EMOIP distribution from complete samples. These classical estimators include the percentiles estimators, maximum likelihood estimators, least squares estimators, maximum product spacing estimators, and weighted least-squares estimators. The likelihood estimators of the parameters under type-I and type-II censoring schemes are discussed. Simulation results were conducted, for various parameter combinations and different sample sizes, to compare the performance of the EMOIP estimation methods under complete and censored samples.… More >

  • Open Access


    On Modeling the Medical Care Insurance Data via a New Statistical Model

    Yen Liang Tung1, Zubair Ahmad2,*, G. G. Hamedani3

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 113-126, 2021, DOI:10.32604/cmc.2020.012780

    Abstract Proposing new statistical distributions which are more flexible than the existing distributions have become a recent trend in the practice of distribution theory. Actuaries often search for new and appropriate statistical models to address data related to financial and risk management problems. In the present study, an extension of the Lomax distribution is proposed via using the approach of the weighted T-X family of distributions. The mathematical properties along with the characterization of the new model via truncated moments are derived. The model parameters are estimated via a prominent approach called the maximum likelihood estimation method. A brief Monte Carlo… More >

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