İstatistik Bilim Dalı Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/4382
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Browsing İstatistik Bilim Dalı Yayın Koleksiyonu by Scopus Q "Q3"
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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: 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.