Yazar "Hamedani, Gholamhossein G." seçeneğine göre listele

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    An alternative distribution to lindley and power lindley distributions with characterizations, different estimation methods and data applications
    (Walter De Gruyter Gmbh, 2020) Korkmaz, Mustafa Çağatay; Hamedani, Gholamhossein G.
    This paper proposes a new extended Lindley distribution, which has a more flexible density and hazard rate shapes than the Lindley and Power Lindley distributions, based on the mixture distribution structure in order to model with new distribution characteristics real data phenomena. Its some distributional properties such as the shapes, moments, quantile function, Bonferonni and Lorenz curves, mean deviations and order statistics have been obtained. Characterizations based on two truncated moments, conditional expectation as well as in terms of the hazard function are presented. Different estimation procedures have been employed to estimate the unknown parameters and their performances are compared via Monte Carlo simulations. The flexibility and importance of the proposed model are illustrated by two real data sets.
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    The exponential lindley odd log-logistic-g family: properties, characterizations and applications
    (Atlantis Press, 2018) Korkmaz, Mustafa Çağatay; Yousof, Haitham M.; Hamedani, Gholamhossein G.
    A new family of distributions called the exponential Lindley odd log-logistic G family is introduced and studied. The new generator generalizes three newly defined G families and also defines two new G families We provide some mathematical properties of the new family. Characterizations based on truncated moments as well as in terms of the hazard function are presented. The maximum likelihood is used for estimating the model parameters. We assess the performance of the maximum likelihood estimators in terms of biases and mean squared errors by means of a simulation study. Finally, the usefulness of the family is illustrated by means of three real data sets. The new model provides consistently better fits than other competitive models for these data sets.
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    On the Burr XII-moment exponential distribution
    (Public Library of Science, 2021) Bhatti, Fiaz Ahmad; Hamedani, Gholamhossein G.; Korkmaz, Mustafa Çağatay; Sheng, Wenhui; Ali, Azeem
    In this study, a new flexible lifetime model called Burr XII moment exponential (BXII-ME) distribution is introduced. We derive some of its mathematical properties including the ordinary moments, conditional moments, reliability measures and characterizations. We employ different estimation methods such as the maximum likelihood, maximum product spacings, least squares, weighted least squares, Cramer-von Mises and Anderson-Darling methods for estimating the model parameters. We perform simulation studies on the basis of the graphical results to see the performance of the above estimators of the BXII-ME distribution. We verify the potentiality of the BXII-ME model via monthly actual taxes revenue and fatigue life applications.
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    On the burr xii-power cauchy distribution: Properties and applications
    (University of Punjab (new Campus), 2021) Bhatti, Fiaz Ahmad; Cordeiro, Gauss Moutinho; Korkmaz, Mustafa Çağatay; Hamedani, Gholamhossein G.
    We introduce a four-parameter lifetime model with flexible hazard rate called the Burr XII gamma (BXIIG) distribution. We derive the BXIIG distribution from (i) the T-X family technique and (ii) nexus between the exponential and gamma variables. The failure rate function for the BXIIG distribution is flexible as it can accommodate various shapes such as increasing, decreasing, decreasing-increasing, increasing-decreasing-increasing, bathtub and modified bathtub. Its density function can take shapes such as exponential, J, reverse-J, left-skewed, right-skewed and symmetrical. To illustrate the importance of the BXIIG distribution, we establish various mathematical properties such as random number generator, ordinary moments, generating function, conditional moments, density functions of record values, reliability measures and characterizations. We address the maximum likelihood estimation for the parameters. We estimate the adequacy of the estimators via a simulation study. We consider applications to two real data sets to prove empirically the potentiality of the proposed model.
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    On the new modified Dagum distribution: Properties and applications
    (Taru Publication, 2020) Bhatti, Fiaz Ahmad; Hamedani, Gholamhossein G.; Korkmaz, Mustafa Çağatay; Ahmad, Munir
    In this paper, a new five parameter extended Dagum model called new modified Dagum (NMD) distribution is proposed. The proposed distribution is flexible as its density contains important sub-models such as modified Dagum, new Dagum, new modified Burr III, modified Burr III, Frechet, modified Frechet and many other distributions. The NMD density function is symmetrical, left-skewed, right-skewed, J, reverse-J and arc. The NMD distribution can produce all types of failure rates such as modified bathtub, bathtub, inverted bathtub, increasing and decreasing. To show the importance of the proposed distribution, we derive mathematical properties such as random number generator, sub-models, ordinary moments, moment generating function, characteristic function, incomplete moments, inequality measures, residual life functions and reliability measures. We characterize the NMD distribution via innovative techniques. We address the maximum likelihood estimation technique for the model parameters. We evaluate the precision of the maximum likelihood estimators via simulation study on the basis of the graphical results. We consider an application to a real data set to clarify the potentiality and utility of the NMD model. We establish empirically that the proposed model is suitable for survival times of patient's application. We apply goodness of fit statistics and graphical tools to examine the adequacy of the NMD distribution.
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    The type I quasi lambert family: properties, characterizations and different estimation methods
    (Univ Punjab, 2021) Hamedani, Gholamhossein G.; Korkmaz, Mustafa Çağatay; Butt, Nadeem Shafique; Yousof, Haitham M.
    A new G family of probability distributions called the type I quasi Lambert family is defined and applied for modeling real lifetime data. Some new bivariate type G families using "Farlie-Gumbel-Morgenstern copula", "modified Farlie-Gumbel-Morgenstern copula", "Clayton copula" and "Renyi's entropy copula" are derived. Three characterizations of the new family are presented. Some of its statistical properties are derived and studied. The maximum likelihood estimation, maximum product spacing estimation, least squares estimation, Anderson-Darling estimation and Cramer-von Mises estimation methods are used for estimating the unknown parameters. Graphical assessments under the five different estimation methods are introduced. Based on these assessments, all estimation methods perform well. Finally, an application to illustrate the importance and flexibility of the new family is proposed
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    The unit generalized log Burr XII distribution: properties and application
    (American Institute of Mathematical Sciences, 2021) Bhatti, Fiaz Ahmad; Ali, Azeem; Hamedani, Gholamhossein G.; Korkmaz, Mustafa Çağatay; Ahmad, Munir
    In this paper, a three-parameter bounded unit distribution with a flexible hazard rate called the unit generalized log Burr XII (UGLBXII) distribution is derived. To show the importance of the proposed distribution, we establish some of its mathematical properties such as random number generator, ordinary moments, generalized TL moments, conditional moments, reliability and uncertainty measures. We characterize the UGLBXII distribution via innovative techniques. We also present the bivariate? and multivariate?type distributions via Morgenstern (Mor) family and via Clayton family. Six estimation methods such as the maximum likelihood, maximum product spacings, least squares, weighted least squares, Cramer-von Mises and Anderson-Darling methods are adopted to estimate its unknown parameters. We perform simulation studies on the basis of the graphical results to see the performance of the above estimators. Two real data sets are considered to prove the empirical superiority of the proposed model. © 2021 the Author(s), licensee AIMS Press.