İstatistik Bilim Dalı Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/4382
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Conference Object Forecasting The Natural Gas Demand At New Locations A Case Study For Turkey(2005) Türker Bayrak, Özlem; Köksal, Gülser; Okandan, EnderConference Object A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations(2017) Dener Akkaya, Ayşen; Türker Bayrak, ÖzlemIn recent years, it is seen in many time series applications that innovations are non-normal. In this situation, it is known that the least squares (LS) estimators are neither efficient nor robust and maximum likelihood (ML) estimators can only be obtained numerically which might be problematic. The estimation problem is considered newly through different distributions by the use of modified maximum likelihood (MML) estimation technique which assumes the shape parameter to be known. This becomes a drawback in machine data processing where the underlying distribution cannot be determined but assumed to be a member of a broad class of distributions. Therefore, in this study, the shape parameter is assumed to be unknown and the MML technique is combined with Huber’s estimation procedure to estimate the model parameters of autoregressive (AR) models of order 1, named as adaptive modified maximum likelihood (AMML) estimation. After the derivation of the AMML estimators, their efficiency and robustness properties are discussed through simulation study and compared with both MML and LS estimators. Besides, two test statistics for significance of the model are suggested. Both criterion and efficiency robustness properties of the test statistics are discussed, and comparisons with the corresponding MML and LS test statistics are given. Finally, the estimation procedure is generalized to AR(q) models.Article Assessment of the Use of AutoCAD in Mechanical Engineering Technical Drawing Education(2017) Akyürek, Turgut: AutoCAD is one of the widely used software tools in engineering education. In this study, a general assessment of AutoCAD for the usage in the mechanical engineering technical drawing education is made. AutoCAD is assessed in terms of the fulfilment of the requirements defined for the main two technical drawing courses. AutoCAD is assessed in terms of its capability in meeting the requirements of the technical drawing coursesConference Object Global Krizler için Doğrusal Profillere Dayalı Kontrol Şemaları ile Oluşturulan Erken Uyarı Sistemi(2015) Türker Bayrak, Özlem; Aytaçoğlu, Burcu; Yüksel Haliloğlu, EbruConference Object Citation - Scopus: 1Survey and Evaluation on Modelling of Next-Day Electricity Prices(Springer New York LLC, 2014) Bayrak, Ö.T.; Weber, G.-W.; Yıldırım, M.H.Conference Object Estimation of AR(1) Model Having Generalized Logistic Disturbances(2020) Akkaya, Ayşen; Türker Bayrak, ÖzlemNon-normality is becoming a common feature in real life applications. Using non-normal disturbances in autoregressive models induces non-linearity in the likelihood equations so that maximum likelihood estimators cannot be derived analytically. Thus, modified maximum likelihood estimation (MMLE) technique is introduced in literature to overcome this difficulty. However, this method assumes the shape parameter to be known which is not realistic in real life. Recently, for unknown shape parameter case, adaptive modified maximum likelihood estimation (AMMLE) method that combines MMLE with Huber estimation method is suggested in literature. In this study, we adopt AMMLE method to AR(1) model where the disturbances are Generalized Logistic distributed. Although Huber M-estimation is not applicable to skew distributions, the AMMLE method extends Huber type work to skew distributions. We derive the estimators and evaluate their performance in terms of efficiBook Part A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations(Springer, 2018) Dener Akkaya, Ayşen; Türker Bayrak, ÖzlemIn recent years, it is seen in many time series applications that innovations are non-normal. In this situation, it is known that the least squares (LS) estimators are neither efficient nor robust and maximum likelihood (ML) estimators can only be obtained numerically which might be problematic. The estimation problem is considered newly through different distributions by the use of modified maximum likelihood (MML) estimation technique which assumes the shape parameter to be known. This becomes a drawback in machine data processing where the underlying distribution cannot be determined but assumed to be a member of a broad class of distributions. Therefore, in this study, the shape parameter is assumed to be unknown and the MML technique is combined with Huber’s estimation procedure to estimate the model parameters of autoregressive (AR) models of order 1, named as adaptive modified maximum likelihood (AMML) estimation. After the derivation of the AMML estimators, their efficiency and robustness properties are discussed through simulation study and compared with both MML and LS estimators. Besides, two test statistics for significance of the model are suggested. Both criterion and efficiency robustness properties of the test statistics are discussed, and comparisons with the corresponding MML and LS test statistics are given. Finally, the estimation procedure is generalized to AR(q) models.Article Normal Olmayan Dağılımlar Altında Tahminin Basit Doğrusal Profil İzleme Üzerine Etkisi(2019) Bayrak, Özlem Türker; Aytaçoğlu, BurcuSon yıllarda, bir ürün veya sürecin kalitesinin tepki ve açıklayıcı değişken(ler) arasındaki ilişkinin fonksiyonu ile ifade edildiği profillerin izlenmesi için pek çok kalite şeması önerilmiştir. Bu yöntemlerin çoğu Faz II analizlerinde kontrol parametre değerlerinin bilindiğini ve artıkların normal dağıldığını varsaymaktadır. Oysaki uygulamada parametreler Faz I analizlerinde tahmin edilir ve artıklar normal olmayabilir. Bu çalışmada simülasyon ile artıkların t dağıldığı ve parametrelerin tahmin edildiği durumlarda basit doğrusal profillerin izlenmesi için önerilen T2, EWMA-R ve EWMA-3 yöntemlerinin performansları değerlendirilmiştir. Performans ölçüsü olarak hem ortalama koşu uzunluğu hem de koşu uzunluğu standart sapması dikkate alınmıştır. En sonunda uygulayıcılar için bazı öneriler tablo halinde özetlenmiştir.Article Doğrusal Profillere Dayalı Kontrol Şemalarının Ekonomide Erken Uyarı Sistemi Oluşturmak İçin Uyarlanması: Enerji Sektöründe Bir Pilot Çalışma(2019) Haliloğlu, Ebru Yüksel; Bayrak, Özlem Türker; Aytaçoğlu, BurcuBu çalışmada, küresel krizleri öngörebilmek ve dolayısıyla karar alıcılar tarafından önleyici aksiyonlar alınabilmesi amacıyla erken uyarı sistemi oluşturmak üzere doğrusal profil için kontrol şemaları adapte edilmiştir. Bu doğrultuda, gayri safi yurt içi hasıla (GSYH) ile G8 ve gelişmekte olan büyük ülkelerin 1980-2012 yıllarındaki enerji tüketimi arasındaki ilişki incelenmiştir. Faz I analizi model parametrelerinin zaman içinde otokorelasyon içerdiğini göstermiştir. Dolayısıyla, bu otokorelasyonu dikkate alan, doğrusal profiller için Shewhart ve EWMA şemaları kullanılmış ve EWMA şemasının daha iyi olduğu tespit edilmiştir. 2008 küresel krizinin tespit edilebildiği ancak yerel Asya krizinin tespit edilemediği görülmüştür. Bu çalışma, hem doğrusal profillerin izlenmesi için geliştirilen kontrol şemalarını erken uyarı sistemi oluşturmak amacıyla kullanan hem de açıklayıcı değişkenlerin (x-değerleri) profilden profile çeşitlilik arz etmesi ile profiller arası korelasyonu da dikkate alan ilk çalışmadır.Conference Object Citation - Scopus: 1Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution(Association for Computing Machinery, 2019) Yentür, B.; Bayrak, Ö.T.; Akkaya, A.D.In this paper, we consider the autoregressive models where the error term is non-normal; specifically belongs to a long-tailed symmetric distribution family since it is more relevant in practice than the normal distribution. It is known that least squares (LS) estimators are neither efficient nor robust under non-normality and maximum likelihood (ML) estimators cannot be obtained explicitly and require a numerical solution which might be problematic. In recent years, modified maximum likelihood (MML) estimation is developed to overcome these difficulties. However, this method requires that the shape parameter is known which is not realistic in machine data processing. Therefore, we use adaptive modified maximum likelihood (AMML) technique which combines MML with Huber’s estimation procedure so that the shape parameter is also estimated. After derivation of the AMML estimators, their efficiency and robustness properties are discussed through a simulation study and compared with MML and LS estimators. © 2019 Association for Computing Machinery.