Yazar "Genç, Murat" seçeneğine göre listele

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    A study on comparison of convex and non-convex penalized regression methods
    (Balıkesir Üniversitesi, 2024) Genç, Murat
    In linear regression, penalized regression methods are used to obtain more accurate predictions depending on the structure of the data set. In addition, it is possible to determine the explanatory variables associated with the response variable by using penalized regression methods. In this study, the performances of ridge, LASSO, elastic net, adaptive LASSO convex penalized regression methods and SCAD and MCP non-convex penalized regression methods are compared depending on the properties of the true coefficient vector based on simulation studies. While the mean squared error on the test set is used to compare the prediction performance of the models based on the methods, the false classification rate, false positive rate and active set sizes are obtained to compare the performance of the methods in variable selection. According to the simulation studies, it has been observed that the structure of the true coefficient vector has a remarkable effect on the performance of the models created by convex and non-convex penalized regression methods.
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    An Extended UEHL Distribution: Properties and Applications
    (2024) Genç, Murat; Özbilen, Ömer
    This study introduces a new distribution, a Lehmann-type exponentiated distribution, which is built upon the unit exponentiated half-logistic distribution. The analytical characteristics of the proposed distribution, like moments, moment-generating function, quantiles, and stress-strength reliability, are explored in detail. The renowned maximum likelihood estimation method is employed for the statistical inference of the distribution’s parameters. A computer experiment is run to explore the performance of the maximum likelihood estimates of the distribution parameters under diverse scenarios. Additionally, the practicality and efficacy of the distribution are illustrated through a numerical example using a real-world dataset.
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    An Extension of the UEHL Distribution Based on the DUS Transformation
    (2023) Genç, Murat; Özbilen, Ömer
    In this study, we propose a new distribution based on the Dinesh, Umesh, and Sanjay (DUS) transformation by using the Unit Exponentiated Half-Logistic (UEHL) distribution as the baseline distribution, a member of the family of proportional hazard rate models. Moreover, we study several properties, such as moments, skewness, kurtosis, stress-strength reliability, and likelihood ratio ordering. Further, we discuss the statistical inference on the parameters of the proposed distribution by the maximum likelihood estimation (MLE) method. Besides, we conduct a simulation based on the new distribution to investigate the behavior of the maximum likelihood estimates in various conditions. Furthermore, we present a numerical example to show the performance of the distribution on a real-life data set. Finally, we discuss the need for further research.
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    Bir Simülasyon Çalışması ile Cezalı Regresyon Yöntemlerinin Karşılaştırılması
    (2022) Genç, Murat
    Veri kümesinde çoklu iç ilişki problemi olması durumunda kararlı katsayı tahminleri elde etmek için sıklıkla cezalı regresyon yöntemleri kullanılır. Ayrıca bu yöntemler uygulanan ceza teriminin yapısına bağlı olarak otomatik değişken seçimi de yapabilmektedir. Bu çalışmada literatürde yaygın kullanım alanı bulan ridge, LASSO, elastik net ve uyarlanabilir LASSO cezalı regresyon yöntemlerinin gerçek katsayı vektörünün yapısına bağlı olarak simülasyon çalışmaları yoluyla performanslarının ayrıntılı olarak karşılaştırılması yapılmıştır. Çalışmada karşılaştırma kriteri olarak test kümesi üzerinde hata kareler ortalaması, yanlış sınıflama oranı, yanlış pozitif oranı ve aktif küme büyüklükleri kullanılmıştır. Simülasyon çalışmaları, gerçek katsayı vektörünün yapısının yöntemlerin ortaya çıkardığı model performansı üzerinde önemli etkisinin olduğunu göstermektedir.
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    Dönüştürülmüş Birim Üstel Yarı Lojistik Dağılım ve Uygulamaları
    (Ordu University, 2024) Genç, Murat; Özbilen, Ömer
    Orantılı tehlike hızı model ailesinin bir üyesi olan birim üstel yarı lojistik dağılım temel dağılım olarak kullanılarak, dönüştürülmüş (transmuted) birim üstel yarı lojistik dağılım olarak adlandırılan yeni bir dağılım önerilmiştir. Önerilen dağılımın momentler, moment çıkaran fonksiyon, kantil fonksiyonu ve stres-mukavemet güvenilirliği gibi istatistiksel özellikleri bu çalışmada ayrıntılı olarak incelenmiştir. Dağılım parametrelerinin istatistiksel çıkarımı için maksimum olabilirlik tahmin yöntemi tartışılmıştır. Maksimum olabilirlik tahminlerinin çeşitli koşullar altındaki davranışını araştırmak için yeni dağılıma dayalı bir simülasyon çalışması yapılmıştır. Ayrıca, bir başarısızlık-zaman veri kümesi üzerinde dağılımın performansını göstermek için sayısal bir örnek sunulmuştur.
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    Exponentiated UEHL Distribution: Properties and Applications
    (2023) Genç, Murat; Özbilen, Ömer
    In this paper, we propose a distribution for modeling data defined on a unit interval using an exponentiated transformation. The new distribution is based on the unit exponential half-logistic distribution, a member of proportional hazard models. Several measures of the statistical characterization of the distribution are discussed. The statistical inference of the parameters of the proposed distribution is studied by the maximum likelihood method. To explore the properties of the maximum likelihood estimates of the parameters, simulation studies are carried out under various scenarios. Furthermore, a real dataset is analyzed to demonstrate the performance of the distribution.
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    The Effect of the Second Stage Estimator on Model Performance in Post-LASSO Method
    (2023) Genç, Murat; Özbilen, Ömer
    Penalized linear regression methods are used for the accurate prediction of new observations and to obtain interpretable models. The performance of these methods depends on the properties of the true coefficient vector. The LASSO method is a penalized regression method that can simultaneously perform coefficient shrinkage and variable selection in a continuous process. Depending on the structure of the dataset, different estimators have been proposed to overcome the problems faced by LASSO. The estimation method used in the second stage of the post-LASSO two-stage regression method proposed as an alternative to LASSO has a considerable effect on model performance. In this study, the performance of the post-LASSO is compared with classical penalized regression methods ridge, LASSO, elastic net, adaptive LASSO and Post-LASSO by using different estimation methods in the second stage of the post-LASSO. In addition, the effect of the magnitude and position of the signal values in the real coefficient vector on the performance of the models obtained by these methods is analyzed. The mean squared error and standard deviation of the predictions calculated on the test set are used to compare the prediction performance of the models, while the active set sizes are used to compare their performance in variable selection. According to the findings obtained from the simulation studies, the choice of the second-stage estimator and the structure of the true coefficient vector significantly affect the success of the post-LASSO method compared to other methods.