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  • Open Access

    ARTICLE

    Generalized Truncated Fréchet Generated Family Distributions and Their Applications

    Ramadan A. ZeinEldin1,2, Christophe Chesneau3,*, Farrukh Jamal4, Mohammed Elgarhy5, Abdullah M. Almarashi6, Sanaa Al-Marzouki6

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 791-819, 2021, DOI:10.32604/cmes.2021.012169 - 21 January 2021

    Abstract Understanding a phenomenon from observed data requires contextual and efficient statistical models. Such models are based on probability distributions having sufficiently flexible statistical properties to adapt to a maximum of situations. Modern examples include the distributions of the truncated Fréchet generated family. In this paper, we go even further by introducing a more general family, based on a truncated version of the generalized Fréchet distribution. This generalization involves a new shape parameter modulating to the extreme some central and dispersion parameters, as well as the skewness and weight of the tails. We also investigate the More >

  • Open Access

    ARTICLE

    Topp-Leone Odd Fréchet Generated Family of Distributions with Applications to COVID-19 Data Sets

    Sanaa Al-Marzouki1, Farrukh Jamal2, Christophe Chesneau3,*, Mohammed Elgarhy4

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 437-458, 2020, DOI:10.32604/cmes.2020.011521 - 18 September 2020

    Abstract Recent studies have pointed out the potential of the odd Fréchet family (or class) of continuous distributions in fitting data of all kinds. In this article, we propose an extension of this family through the so-called “Topp-Leone strategy”, aiming to improve its overall flexibility by adding a shape parameter. The main objective is to offer original distributions with modifiable properties, from which adaptive and pliant statistical models can be derived. For the new family, these aspects are illustrated by the means of comprehensive mathematical and numerical results. In particular, we emphasize a special distribution with More >

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