İstatistik Bilim Dalı Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/4382

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Now showing 1 - 7 of 7

Citation - WoS: 22
Citation - Scopus: 25
Classification 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
Fuzzy 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.
Citation - WoS: 1
Citation - Scopus: 1
Effect of Estimation Under Nonnormality on the Phase Ii Performance of Linear Profile Monitoring Approaches
(Wiley, 2019) Aytacoglu, Burcu; Bayrak, Ozlem Turker
The 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.
Citation - WoS: 9
Electricity Price Modelling for Turkey
(Springer-verlag Berlin, 2012) Ozmen, Ayse; Bayrak, Ozlem Turker; Weber, Gerhard Wilhelm; Yildirim, Miray Hanim
This 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.
Citation - Scopus: 1
Inference of Autoregressive Model With Stochastic Exogenous Variable Under Short-Tailed Symmetric Distributions
(Springer international Publishing Ag, 2018) Bayrak, Ozlem Tuker; Akkaya, Aysen Dener
In 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.
Citation - WoS: 2
Citation - Scopus: 1
Inter-Laboratory Comparison Scheme for Fuel Sector, Labkar in Turkey
(Springer, 2009) Bayrak, Ozlem Turker; Okandan, Ender; Uckardes, Hale
Fuel 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.
Citation - Scopus: 1
Linear Contrasts in One-Way Classification Ar(1) Model With Gamma Innovations
(Hacettepe Univ, Fac Sci, 2016) Senoglu, Birdal; Bayrak, Ozlem Turker
In 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.
Citation - WoS: 1
Citation - Scopus: 2
Sample Design and Allocation for Random Digit Dialling
(Springer, 2005) Ayhan, HO; Islam, MQ
Sample 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.

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