Yazar "Ali, M. Masoom" seçeneğine göre listele

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    Odd lindley-lomax model: Statistical properties and applications
    (Univ Punjab, 2019) Ali, M. Masoom; Korkmaz, Mustafa Çağatay; Yousof, Haitham M.; Butt, Nadeem Shafique
    In this work, we focus on some new theoretical and computational aspects of the Odd LindleyLomax model. The maximum likelihood estimation method is used to estimate the model parameters. We show empirically the importance and flexibility of the new model in modeling two types of aircraft windshield lifetime data. This model is much better than exponentiated Lomax, gamma Lomax, beta Lomax and other Lomax models so that the Odd Lindley-Lomax lifetime model is a good alternative to these models in modeling aircraft windshield data. A Monte Carlo simulation study is used to assess the performance of the maximum likelihood estimators.
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    Some theoretical and computational aspects of the odd lindley fréchet distribution
    (Aktüerya Derneği, 2017) Korkmaz, Mustafa Çağatay; Yousof, Haitham M.; Ali, M. Masoom
    In this article, we study an extension of the Fréchet model by using the the odd Lindley-G family of distributions, which was introduced by [17]. Its some statistical properties such as quantile function, density shapes, moments, generating functions and order statistics are obtained. We estimate its parameters by maximum likelihood method. The Monte Carlo simulation is used for assessing the performance of the maximum likelihood method. The usefulness of the odd Lindley Fréchet model is illustrated by means of three real data sets.
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    The marshall-olkin generalized G poisson family of distributions
    (ISOSS PUBLICATIONS, 2018) Korkmaz, Mustafa Çağatay; Yousof, Haitham M.; Hamedani G.G.; Ali, M. Masoom
    In this paper, we propose a new class of lifetime distributions called the Marshall-Olkin Generalized G Poisson family. The proposed family of distributions is constructed by compounding the Marshall-Olkin Generalized distribution with the truncated Poisson distribution. It can provide better fits than some of the known lifetime distributions and this fact represents a good characterization of this new family. Some useful characterizations for the new family are presented. The maximum likelihood method is used for estimating the model parameters. The importance and flexibility of the new family are illustrated by means of an application to a real data set.