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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 the Genesis of the Marshall-Olkin Family of Distributions via the T-X Family Approach: Statistical Modeling

    Yang Zhenwu1, Zubair Ahmad2,*, Zahra Almaspoor2, Saima K. Khosa3

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 753-760, 2021, DOI:10.32604/cmc.2021.012393

    Abstract In the last couple of years, there Has been an increased interest among the statisticians to define new families of distributions by adding one or more additional parameter(s) to the baseline distribution. In this regard, a number of families have been introduced and studied. One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions. Whenever, we see a new method, the natural questions come in to mind are (i) what are the genesis of the newly proposed method and (ii) how did the proposed method is obtained.… More >

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