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Öğe A K-Nearest Neighbor Based Approach for Determining the Weight Restrictions in Data Envelopment Analysis(TÜIK, 2013) Hayat, Elvan Aktürk; Alpay, OlcayData Envelopment Analysis (DEA), a method commonly used to measure the efficiency is becoming an increasingly popular management tool. On the contrary to classical efficiency approaches, the most important advantage of DEA is that researchers can determine the weight restrictions of input and output variables. Variable selection and determination of weight restrictions are important issues in DEA. This work investigates the use of K-nearest neighbor (KNN) algorithm in the definition of weight restrictions for DEA. With this purpose a new approach based on KNN is proposed. Applications are constructed with empirical and real data sets depending on the specific constraints. Performance scores were calculated for both KNN based restricted and unrestricted DEA models and the results are interpreted.Öğe Accounting for Zero Inflation of Mussel Parasite Counts Using Discrete Regression Models(2017) Cankaya, Emel; Alpay, Olcay; Ozer, AhmetIn many ecological applications, the absences of species are inevitable due to either detection faults in samples oruninhabitable conditions for their existence, resulting in high number of zero counts or abundance. Usual practice formodelling such data is regression modelling of log(abundance+1) and it is well know that resulting model isinadequate for prediction purposes. New discrete models accounting for zero abundances, namely zero-inflatedregression (ZIP and ZINB), Hurdle-Poisson (HP) and Hurdle-Negative Binomial (HNB) amongst others are widelypreferred to the classical regression models. Due to the fact that mussels are one of the economically most importantaquatic products of Turkey, the purpose of this study is therefore to examine the performances of these four modelsin determination of the significant biotic and abiotic factors on the occurrences of Nematopsis legeri parasiteharming the existence of Mediterranean mussels (Mytilus galloprovincialis L.). The data collected from the threecoastal regions of Sinop city in Turkey showed more than 50% of parasite counts on the average are zero-valued andmodel comparisons were based on information criterion. The results showed that the probability of the occurrence ofthis parasite is here best formulated by ZINB or HNB models and influential factors of models were found to becorrespondent with ecological differences of the regions.Öğe Çoklu Doğrusal Bağlantılı Nadir Olayların Modellenmesinde Lasso ve Ridge Regresyon ile Boosting Algoritmalarının Performans Karşılaştırması(2024) Alpay, OlcayBu çalışma, iki durumlu olayları modellemek için kullanılan makine öğrenmesi tekniklerinde karşılaşılan nadirlik ve “çoklu doğrusal bağlantı” ya da sadece “çoklu bağlantı” olarak tanımlanan sorunu ele alınmaktadır. Çoklu doğrusal bağlantı (ÇDB), bağımsız değişkenler arasında bir ya da birden fazla kuvvetli doğrusal bağımlılık olma durumudur ve bir sorun olarak ortaya çıkar. Üzerinde çalışılan veri içerisinde çoklu doğrusal bağlantı probleminin var olması regresyon katsayılarının varyanslarının büyümesi gibi olumsuz bir sonuca sebebiyet verir. Bu çalışmada, Lasso ve Ridge Regresyon ile GradientBoost, XGBoost, LightGBM ve AdaBoost gibi artırma algoritmaları içeren düzenleme ve ölçeklendirme tekniklerinin, çoklu doğrusal bağlantılı nadir olayların modellenmesinde, algoritmaların performanslarını karşılaştırmak için detaylı bir simülasyon çalışması sunulmaktadır. Simülasyon çalışmasında, verideki dengesizliği ortadan kaldırmak amacıyla yeniden örnekleme yöntemleri kullanılarak sonuçlara etkisi Hata Kareler Ortalaması (HKO), 𝑅2, Hassasiyet (Precision-Prec), Duyarlılık (Recall-Rec) ve Eğri Altında Kalan Alan (Area Under the Curve-AUC) gibi performans metrikleri ve İşlem Karakteristik Eğrisi (Receiver Operating Characteristic- ROC) grafikleri ile araştırılmaktadır. Sonuçlar Lasso, Ridge ve Boosting algoritmalarının ÇDB’ya sahip nadir olayların modellenmesinde hangi yöntemin uygun olduğunu belirlemek açısından katkı sunmaktadır.Öğe Copula approach to select input/output variables for DEA(2017) Alpay, Olcay; Akturk, Elvan HayatDetermination of the input/output variables is an important issue in Data Envelopment Analysis (DEA). Researchers often refer to expert opinions in defining these variables. The purpose of this paper is to propose a new approach to determine the input/output variables, it is important to keep in mind that especially when there is no any priori information about variable selection. This new proposed techniqueis based on a theoretical method which is called "Copula". Copula functions are used for modeling the dependency structure of the variables with each other. Also we use the local dependence function which analyzes the point dependency of variables of copulas to define the input/output variables. To illustrate the usefulness of the proposed approach, we conduct two applications using simulated and real data and compare the efficiencies in DEA. Our results show that new approach gives values close to perfectionÖğe Modelling of Factors Influencing the Citation Counts in Statistics(2022) Alpay, Olcay; Danacioglu, Nazan; Cankaya, EmelCitation is considered as the most popular quality assessment metric for scientific papers, and it is thus important to determine what factors promote the citation count of a paper in comparison to the others in the same field. The main aim of this study is to model the citation counts of the research published in SCI or SCI-Expanded journals of Statistics field with the growing number of scientific works in Turkey. It is well known that the right-skewed nature of the counts makes the classical regression modelling inappropriate, even if the log transformation of counts is applied [1]. Due to the fact that distribution of citation counts involves a great number of zeros, this study serves for an additional aim that is to model the counts with advanced discrete regression models for a more precise prediction [2]. Data collected for this study consist of the citation counts of all scientific papers produced by 261 Statisticians in between 2005-2017. Discrete models varying from Poisson to Zero-Inflated or Hurdle were constructed by possible influential factors, such as the publication age, the number of references, the journal category etc. Predictive performances of alternative discrete models were compared via AIC and Vuong test [3]. Results suggested that Zero Inflated Negative Binomial and Hurdle Negative Binomial mixture models are the best forms to predict the zero inflation of citation counts [4]. In addition, the influential factors of the final model were interpreted to make some suggestions to Statisticians to increase the citation counts of their work.Öğe Performance of prior and weighting bias correction methods for rare event logistic regression under the influence of sampling bias(Taylor & Francis Inc, 2023) Alpay, Olcay; Cankaya, EmelThe problem of classifying events to binary classes has been popularly addressed by Logistic Regression Analysis. However, there may be situations where the most interested class of event is rare such as an infectious disease, earthquake, financial crisis etc. The model of such events tends to focus on the majority class, resulting in the underestimation of probabilities for the rare class. Additionally, the model may incorporate sampling bias if the rare class of the sample is not representative of its population. It is therefore important to investigate whether such rareness is genuine or caused by an improperly drawn sample. We conducted a simulation study by creating three populations with different rarity levels and drawing samples from each of those which are either compatible or incompatible with the actual rare classes of the population. Then, the effect of sampling bias is discussed under the two correction methods of bias due to rareness as suggested by King and Zeng.
