Yazar "Alizadeh, Morad" seçeneğine göre listele

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  • Küçük Resim
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    The generalized odd weibull generated family of distributions: statistical properties and applications
    (Univ Punjab, 2018) Korkmaz, Mustafa Çağatay; Alizadeh, Morad; Yousof, Haitham M.; Butt, Nadeem Shafique
    In this work, we propose a new class of lifetime distributions calledthe generalized odd Weibull generatedfamily. It can provide better fits than some of the well known lifetime models and this fact represents a good characterization of this family. Some of its mathematical properties are derived. The maximum likelihood method is used for estimating the model parameters. We study the behaviour of the estimators by means of two Monte Carlo simulations. The importance of the family illustrated by means of two applications to real data sets.
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    The log exponential-power distribution: Properties, estimations and quantile regression model
    (MDPI, 2021) Korkmaz, Mustafa Çağatay; Altun, Emrah; Alizadeh, Morad; El-Morshedy, Mahmoud
    Recently, bounded distributions have attracted attention. These distributions are frequently used in modeling rate and proportion data sets. In this study, a new alternative model is proposed for modeling bounded data sets. Parameter estimations of the proposed distribution are obtained via maximum likelihood method. In addition, a new regression model is defined under the proposed distribution and its residual analysis is examined. As a result of the empirical studies on real data sets, it is observed that the proposed regression model gives better results than the unit-Weibull and Kumaraswamy regression models.
  • Küçük Resim
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    A new flexible lifetime model with log-location regression modeling, properties and applications
    (Taru Publication, 2019) Korkmaz, Mustafa Çağatay; Altun, Emrah; Alizadeh, Morad; Yousof, Haitham M.
    In this paper, we propose a new lifetime model for modeling fatigue lifetime data. Some of its statistical properties are obtained. The method of maximum likelihood is used to estimate the model parameters. Simulation study is given to demonstrate the maximum likelihood estimators of the parameters of proposed model. Moreover, a new log-location-scale regression model is introduced with its residuals analysis. Three applications to real data sets are given to prove the usefulness of proposed model in real data modeling.