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, Ender; 56416Conference Object A New Estimation Technique for AR(1) Model with Long-Tailed Symmetric Innovations(2017) Dener Akkaya, Ayşen; Türker Bayrak, Özlem; 56416In 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; Makine Mühendisliği: 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, Ebru; 56416Conference Object Citation - Scopus: 1Survey and evaluation on modelling of next-day electricity prices(Springer New York LLC, 2014) Yıldırım, M.H.; Yıldırım, Miray Hanım; Bayrak, Ö.T.; Weber, G.-W.; 56416; Endüstri MühendisliğiConference Object Estimation of AR(1) Model Having Generalized Logistic Disturbances(2020) Akkaya, Ayşen; Türker Bayrak, Özlem; 56416Non-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, Özlem; 56416In 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 Effect of Estimation on Simple Linear Profile Monitoring under Non-normality(2019) Bayrak, Özlem Türker; Aytaçoğlu, Burcu; 56416Son 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 Linear Profile Monitoring Adapted to Construct Early Warning System in Economics: A Pilot Study From Energy Sector(2019) Haliloğlu, Ebru Yüksel; Bayrak, Özlem Türker; Aytaçoğlu, Burcu; 56416Bu 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: 0Adaptive Estimation of Autoregressive Models Under Long-Tailed Symmetric Distribution(Association for Computing Machinery, 2019) Yentür, B.; Bayrak, Ö.T.; Akkaya, A.D.; 56416In 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.Article Citation - WoS: 0Citation - Scopus: 1Linear contrasts in one-way classification AR(1) model with gamma innovations(Hacettepe Univ, Fac Sci, 2016) Senoglu, Birdal; Bayrak, Ozlem Turker; 56416In this study, the explicit estimators of the model parameters in oneway classification AR(1) model with gamma innovations are derived by using modified maximum likelihood (MML) methodology. We also propose a new test statistic for testing linear contrasts. Monte Carlo simulation results show that the MML estimators have higher efficiencies than the traditional least squares (LS) estimators and the proposed test has much better power and robustness properties than the normal theory test.Conference Object Citation - WoS: 20Citation - Scopus: 23Classification Models Based On Tanaka's Fuzzy Linear Regression Approach: the Case of Customer Satisfaction Modeling(Ios Press, 2010) Sekkeli, Gizem; Koksal, Gulser; Batman, Inci; Bayrak, Ozlem Turker; 56416Fuzzy linear regression (FLR) approaches are widely used for modeling relations between variables that involve human judgments, qualitative and imprecise data. Tanaka's FLR analysis is the first one developed and widely used for this purpose. However, this method is not appropriate for classification problems, because it can only handle continuous type dependent variables rather than categorical. In this study, we propose three alternative approaches for building classification models, for a customer satisfaction survey data, based on Tanaka's FLR approach. In these models, we aim to reflect both random and fuzzy types of uncertainties in the data in different ways, and compare their performances using several classification performance measures. Thus, this study contributes to the field of fuzzy classification by developing Tanaka based classification models.Article Citation - WoS: 1Citation - Scopus: 2Sample design and allocation for random digit dialling(Springer, 2005) Ayhan, HO; Islam, MQSample design and sample allocation methods are developed for random digit dialling in household telephone surveys. The proposed method is based on a two-way stratification of telephone numbers. A weighted probability proportional to size sample allocation technique is used, with auxiliary variables about the telephone coverage rates, within local telephone exchanges of each substrata. This makes the sampling design nearly "self-weighting" in residential numbers when the prior information is well assigned. A computer program generates random numbers for the local areas within the existing phone capacities. A simulation study has shown greater sample allocation gain by the weighted probabilities proportional to size measures over other sample allocation methods. The amount of dialling required to obtain the sample is less than for proportional allocation. A decrease is also observed on the gain in sample allocation for some methods through the increasing sample sizes.Conference Object Citation - WoS: 9Electricity Price Modelling for Turkey(Springer-verlag Berlin, 2012) Yildirim, Miray Hanim; Yıldırım, Miray Hanım; Ozmen, Ayse; Bayrak, Ozlem Turker; Weber, Gerhard Wilhelm; 56416; Endüstri MühendisliğiThis paper presents customized models to predict next-day's electricity price in short-term periods for Turkey's electricity market. Turkey's electricity market is evolving from a centralized approach to a competitive market. Fluctuations in the electricity consumption show that there are three periods; day, peak, and night. The approach proposed here is based on robust and continuous optimization techniques, which ensures achieving the optimum electricity price to minimize error in periodic price prediction. Commonly, next-day's electricity prices are forecasted by using time series models, specifically dynamic regression model. Therefore electricity price prediction performance was compared with dynamic regression. Numerical results show that CMARS and RCMARS predicts the prices with 30% less error compared to dynamic regression.Conference Object Citation - WoS: 2Citation - Scopus: 1Inter-laboratory comparison scheme for fuel sector, LABKAR in Turkey(Springer, 2009) Bayrak, Ozlem Turker; Okandan, Ender; Uckardes, Hale; 56416Fuel sector is one of the powerful sectors in Turkish industry. The implementation of a new law for regulating the fuel sector had enforced the quality control of fuels sold to public. This resulted in several accredited fuel-testing laboratories to emerge. Thus, a scheme to evaluate their proficiency in measurements became an important requirement. The inter-laboratory comparison scheme LABKAR for gasoline, diesel oil, LPG, lubricating oil and biodiesel samples have evolved to fulfill this need. In this paper, LABKAR is introduced; the results obtained from the program are analyzed and discussed. The kernel densities of the participants' results show that the use of robust mean as a consensus value is appropriate for fuel samples. Although the number of rounds is not enough to derive strict conclusions, it is seen that the performance of the scheme based on the standard deviations and coefficient of variations is improving in each round. It has been observed that the number of laboratories receiving "action" or "warning" is decreasing, which indicates that they are benefiting from the scheme.Article Citation - WoS: 0Citation - Scopus: 1Inference of Autoregressive Model with Stochastic Exogenous Variable Under Short-Tailed Symmetric Distributions(Springer international Publishing Ag, 2018) Bayrak, Ozlem Tuker; Akkaya, Aysen Dener; 56416In classical autoregressive models, it is assumed that the disturbances are normally distributed and the exogenous variable is non-stochastic. However, in practice, short-tailed symmetric disturbances occur frequently and exogenous variable is actually stochastic. In this paper, estimation of the parameters in autoregressive models with stochastic exogenous variable and non-normal disturbances both having short-tailed symmetric distribution is considered. This is the first study in this area as known to the authors. In this situation, maximum likelihood estimation technique is problematic and requires numerical solution which may have convergence problems and can cause bias. Besides, statistical properties of the estimators can not be obtained due to non-explicit functions. It is also known that least squares estimation technique yields neither efficient nor robust estimators. Therefore, modified maximum likelihood estimation technique is utilized in this study. It is shown that the estimators are highly efficient, robust to plausible alternatives having different forms of symmetric short-tailedness in the sample and explicit functions of data overcoming the necessity of numerical solution. A real life application is also given.Article Citation - WoS: 0Citation - Scopus: 0Effect of estimation under nonnormality on the phase II performance of linear profile monitoring approaches(Wiley, 2019) Aytacoglu, Burcu; Bayrak, Ozlem Turker; 56416The number of studies about control charts proposed to monitor profiles, where the quality of a process/product is expressed as function of response and explanatory variable(s), has been increasing in recent years. However, most authors assume that the in-control parameter values are known in phase II analysis and the error terms are normally distributed. These assumptions are rarely satisfied in practice. In this study, the performance of EWMA-R, EWMA-3, and EWMA-3(d(2)) methods for monitoring simple linear profiles is examined via simulation where the in-control parameters are estimated and innovations have a Student's t distribution or gamma distribution. Instead of the average run length (ARL) and the standard deviation of run length, we used average and standard deviation of the ARL as performance measures in order to capture the sampling variation among different practitioners. It is seen that the estimation effect becomes more severe when the number of phase I profiles used in estimation decreases, as expected, and as the distribution deviates from normality to a greater extent. Besides, although the average ARL values get closer to the desired values as the amount of phase I data increases, their standard deviations remain far away from the acceptable level indicating a high practitioner-to-practitioner variability.
