The Impact of Macro-Economic Drivers in Housing Markets: The US Cas

This paper analyzes the effect of macro-economic, financial and commodity market indicators on housing markets. We compare the efficiency of the models generated by Generalized Linear Models (GLM) and Multivariate Adaptive Regression Splines (MARS) according to method free measures for estimating the housing market trend. These models are used for the first time to identify the influence of macro-economic indicators on housing markets and the estimation of the trend in housing markets to our best knowledge. The empirical analysis focuses on the US housing market, and the illustration of the proposed models is done through the monthly historical realizations of S\&P/Case-Shiller National Home Price Index (HPI) and the US macro-economic indicators over the period from 1999-January to 2018-June. It contributes to the literature by highlighting the interaction between macro-economic indicators and housing markets and analyzing the mechanism of housing markets. The findings indicate that the house price trends are estimated with more accuracy and these models capture the joint influence of explanatory variables. Further, the MARS method is shown to outperform GLM compared to the prediction and forecasting power

Kalite iyileştirmede veri madenciliği kullanımı ve geliştirilmesi

TÜBİTAK MAG30.06.2009Bu projede amaç, sanayi kuruluşlarında ürün ve süreçlerin kalitesini iyileştirmeye yönelik veri madenciliği (VM) yaklaşımlarını belirlemek ve daha etkili yaklaşımlar geliştirmektir. Projede imalat sanayi kuruluşlarının ürün ve süreçlerinin kalitesini iyileştirme ile ilgili kalitenin tanımlanması, tahmin edilmesi, sınıflandırılması ve parametrelerinin optimizasyonu problemleri ele alınmıştır. Bu problemlerin çözümü için veri hazırlama ve önişlemenin yanısıra kümeleme, tahmin etme, sınıflandırma, birliktelik analizi ve optimizasyon VM işlevlerinin gerekli olabileceği belirlenmiştir. Bu kapsam dahilinde geniş bir literatür taraması yapılmış ve değişik imalat sektörlerinde etkinlik gösteren altı kuruluş ziyaret edilmiştir. Bunlardan üçünün sağladığı veriler üzerinde uygun VM metotları uygulanmış ve sonuçlar karşılaştırılmıştır. Bu karşılaştırma sonucunda belli VM işlevleri için kalite iyileştirme amaçlarına en uygun VM metotları belirlenmiş ve uygulayıcılara önerilmiştir. Projenin yöntem geliştirme kısmında ise uygulama aşamasında karşılaşılan bazı problemlerin giderilmesi ve mevcut yöntemlerin kullanım kolaylığı ve/veya etkililiğinin artırılması yönünde çalışmalar gerçekleştirilmiştir. Sonuçta, kalite verilerinin yeniden örneklenmesi için bir yöntem; parametrik olmayan alternatif bir regresyon yaklaşımı (CMARS); ikili sınıflandırmada kullanımı kolay olan Mahalanobis Taguchi Sistemi metodunun çok sınıf ve ayrıca parametre optimizasyonu için uyarlamalar; bulanık sınıflandırmada kalite verilerine uygun alternatif yaklaşımlar (bulanık regresyona dayalı modeller) ve parametrik olmayan bulanık tahmin etme ve sınıflandırma fonksiyonları; parametre optimizasyonunda çekicilik fonksiyonlarının optimizasyonu için alternatif yaklaşımlar ve birliktelik kurallarının seçimi için bir yöntem geliştirilmiştir. Bu sonuçların ve metotların kalite iyileştirme alanında uygulayıcıların çalışmalarına yön vermesi ve bunların kullanım kolaylığı ile etkililiğini artırması beklenmektedir.The objective of this project is to identify the data mining (DM) approaches that can effectively improve product and process quality in industrial organizations, and to develop more effective approaches. In the project, quality definition, prediction, classification and parameter optimization problems associated with product and process quality improvement in manufacturing industries are considered. For the solution of these problems, clustering, prediction, classification, association and optimization functions of DM as well as data preparation and preprocessing are determined as relevant. A comprehensive literature survey has been performed and six manufacturing companies operating in different sectors have been visited, within this context. Appropriate DM methods are applied on data sets obtained from three of these companies, and the results are compared. As a result, the most appropriate DM methods are suggested for specific DM functions and quality improvement purposes. In the method development part of the project, studies are performed to overcome some problems encountered during the applications, and to increase ease of use and effectiveness of the VM methods. As a result, a resampling method for quality data; an alternative nonparametric approach (CMARS) for regression; adaptations of an easy to use binary classification method, Mahalanobis Taguchi system, to multiple classes and also to parameter optimization; alternative approaches for fuzzy classification of quality data (models based on fuzzy regression) and nonparametric fuzzy functions; alternative approaches for optimization of desirability functions in parameter optimization; and a method for reduction of association rules are developed. It is expected that these results and approaches guide practitioners in quality improvement area, and incease the ease of use and effectiveness of them

Evaluation of subclinical atherosclerosis in obese patients with three noninvasive methods: Arterial stiffness, carotid intima-media thickness, and biomarkers of endothelial dysfunction

ABSTRACT Objective: In this study, we aimed to evaluate subclinical atherosclerosis in patients with obesity who had cardiovascular disease risk indicators such as arterial stiffness, which is evaluated using pulse wave velocity (PWV), carotid intima-media thickness (CIMT), and biomarkers of endothelial dysfunction such as endocan, ADAMTS97, and ADAMTS9. Subjects and methods: Sixty obese subjects, including 23 subjects with body mass index (BMI) ≥ 40, 37 subjects with BMI ≥ 30 but < 40, and 60 age-and sex-matched control subjects, were included in our study. Serum endocan, ADAMTS97, and ADAMTS9 levels as well as PWV and CIMT measurements of the subjects in the obese and control groups were performed. Results: In the obesity group, PWV levels were significantly higher than they were in the control group and endocan levels were significantly lower than they were in the control group. When we compared the obese group with BMI ≥ 40 and the control group, the BMI ≥ 40 group had significantly higher PWV and CIMT levels than the control group had, whereas endocan, ADAMTS7, and ADAMTS9 levels were similar to those of the control group. When we compared the obese group with BMI ≥ 30 < 40 to the control group, endocan levels were lower in the group with BMI ≥ 30 < 40, and PWV and CIMT levels were similar to the control group. Conclusions: We found that arterial stiffness and CIMT increased in obese patients with BMI ≥ 40 and that increased arterial stiffness was associated with age, systolic blood pressure, and HBA1C. In addition, we found that the endocan levels were lower in obese patients than they were in nonobese control individuals

Doğrusal olmayan sağlam regresyon ve sınıflandırmaya Mars ile yeni bir katkı ve bu katkının endüstride kalite kontrolü amaçlı veri madenciliği uygulamaları.

Multivariate adaptive regression spline (MARS) denotes a modern methodology from statistical learning which is very important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. MARS is very useful for high dimensional problems and shows a great promise for fitting nonlinear multivariate functions. MARS technique does not impose any particular class of relationship between the predictor variables and outcome variable of interest. In other words, a special advantage of MARS lies in its ability to estimate the contribution of the basis functions so that both the additive and interaction effects of the predictors are allowed to determine the response variable. The function fitted by MARS is continuous, whereas the one fitted by classical classification methods (CART) is not. Herewith, MARS becomes an alternative to CART. The MARS algorithm for estimating the model function consists of two complementary algorithms: the forward and backward stepwise algorithms. In the first step, the model is built by adding basis functions until a maximum level of complexity is reached. On the other hand, the backward stepwise algorithm is began by removing the least significant basis functions from the model. In this study, we propose not to use the backward stepwise algorithm. Instead, we construct a penalized residual sum of squares (PRSS) for MARS as a Tikhonov regularization problem, which is also known as ridge regression. We treat this problem using continuous optimization techniques which we consider to become an important complementary technology and alternative to the concept of the backward stepwise algorithm. In particular, we apply the elegant framework of conic quadratic programming which is an area of convex optimization that is very well-structured, herewith, resembling linear programming and, hence, permitting the use of interior point methods. The boundaries of this optimization problem are determined by the multiobjective optimization approach which provides us many alternative solutions. Based on these theoretical and algorithmical studies, this MSc thesis work also contains applications on the data investigated in a TÜBİTAK project on quality control. By these applications, MARS and our new method are compared.M.S. - Master of Scienc

Konik çok değişkenli uyarlanabilir regresyon eğrilerinin geliştirilmesi, uzantıları ve modern uygulamaları.

Conic Multivariate Adaptive Regression Splines (CMARS) which has been developed at the Institute of Applied Mathematics, METU, as an alternative approach to the well-known data mining tool Multivariate Adaptive Regression Splines (MARS). CMARS is based on given data and a penalized residual sum of squares for MARS, interpreted as a Tikhonov Regularization problem. CMARS treats this problem by a continuous optimization technique called Conic Quadratic Programming (CQP). This doctoral thesis adapts the CMARS model into a wide frame of advanced methods of statistics and applied mathematics. The first application is using CMARS in Generalized Partial Linear Models (GPLMs), a particular form of a semiparametric model, which extends the Generalized Linear Models (GLMs) in that the usual parametric terms are augmented by a single nonparametric component. We prefer GLMs because of their flexibility to the variety of statistical problems and the availability of software to fit the models. There are different kinds of estimation methods for GPLMs. One of the great advantages of semiparametric models consists of some grouping (linear and nonlinear or parametric and nonparametric) which could be done for the input dimensions (or features) in order to assign appropriate submodels to the groups specifically. In this thesis, for the estimation of the parametric model part, we apply the least-squares estimation. On the other hand, we consider CMARS for the nonparametric part to estimate the smooth function. This new algorithm, called CGPLM, has the advantage of higher speed and less complexity, as it accesses the use of interior point methods. The other extension is the use of CMARS method for the outlier identification problem. For this purposes, we provide a new solution by using regularization and CQP techniques to the mean-shift outlier model, which is considered as a parametric method. After that the proposed method is improved by using CMARS to represent the nonlinear structure in the data. The second track of this doctorate study is the use of CMARS method for the parameter identification of Stochastic Differential Equations (SDEs) driven by Brownian motions and fractional Brownian motions (fBms). Both systems of SDEs with standard multi-dimensional Brownian motions and systems of SDEs having correlated Brownian motions are covered in this thesis. Moreover, we introduce the CMARS method to estimate both the spline coefficients and, especially, the Hurst parameter of the SDEs driven by fBms. The theoretical results of this study may lead new implementations and applications in science, technology and finance. This PhD thesis ends with a conclusion and an outlook to future studies.Ph.D. - Doctoral Progra

Prediction of potential seismic damage using classification and regression trees: a case study on earthquake damage databases from Turkey

Seismic damage estimation is an important key ingredient of seismic loss modeling, risk mitigation and disaster management. It is a problem involving inherent uncertainties and complexities. Thus, it is important to employ robust approaches which will handle the problem accurately. In this study, classification and regression tree approach is applied on damage data sets collected from reinforced concrete frame buildings after major previous earthquakes in Turkey. Four damage states ranging from None to Severe are used, while five structural parameters are employed as damage identifiers. For validation, results of classification analyses are compared against observed damage states. Results in terms of well-known classification performance measures indicate that when the size of the database is larger, the correct classification rates are higher. Performance measures computed for Test data set indicate similar success to that of Train data set. The approach is found to be effective in classifying randomly selected damage data

Plasma Fatty Acid Composition, Estimated Desaturaseand Elongase Acitivities in Obese People

Obezite, yükselmiş yağ asit sirkülasyonu, hipertansiyon, hiperlipidemi, diyabet ve insülin direncinin artmış riski ile ilişkili bulunmuştur. Bu bozukluklardan bazıları yağ asit metabolizmasında bir değişime sebep olabilir. Obez bireylerde artmış plazma yağ asit düzeyleri [î-oksidasyonu hızlandırmaya ve insülin sensivitesini etkilemeye katkısı olabilir. Yağ asit kompozisyonu hastalık riskinin bir göstergesi olarak kullanılabilir çünkü yağ asit kompozisyonunun değişimi kardiyovasküler hastalık ve metabolik hastalıklar ile ilişkili bulunmuştur. Doymamış yağ asitlerini sentezleyen enzimlere desatüraz denir. Delta 9, ve desatüraz enzimleri uzun zincirli yağ asitlerinde spesifik pozisyonlarda çift bağ oluşturmakla görevlidirler. Yağ asitlerinin zincir uzatma işleminden sorumlu olan enzimlere elongaz adı verilir. Bu derlemede obez kişilerde plazma yağ asit içeriği, desatüraz ve elongaz enzim aktiviteleri güncel yayınlara işaret ederek gözden geçirilmiştir.Obesity is associated with an increased risk of insulin resistance, diabetes, hyperlipidemia, hypertension and elevated circulating fatty acids. Some of those abnormalities may be caused by an altered fatty acid metabolism. Increased plasma fatty acids in obese individuals may contribute to accelerate of [i-oxidation and may also affect insulin sensivity. Fatty acid composition is used as an indicator of disease risk, because its alteration has been related to metabolic disease and cardiovascular disease. Desaturase are involved in the endogenous synthesis of polyunsaturated fatty acids. The delta 9, and desaturases introduce double bond at specific positions on long chain fatty acids. Fatty acids of the enzymes responsible for chain extension process is called the elongase. In this paper, we review estimated, addressing the recent studies, plasma fatty acid composition, desaturase and elongase activity in obese people

Ratlarda Akrilamid Kullanımının Antioksidan ve Oksidan Değerleri Üzerine Etkisi

WOS:000321751900009Bu çalışmada, uzun süre akrilamid verilen sıçanlar üzerinde total antioksidan durum (TAS), total oksidan durum (TOS) ve iskemi modifiye albuminin (IMA) serum düzeylerinin nasıl değiştiğinin araştırılması amaçlanmıştır. Çalışmada 65-75 g ağırlığında ve yaşları 3-4 haftalık 25 erkek ve 25 dişi Wistar cinsi sıçanlar kullanılmıştır. Hayvanlar 90 gün boyunca standart sıçan yemi ile beslenmişlerdir. Bununla beraber, günlük tüketecekleri içme suyuna 2 mg/kg/gün ve 5 mg/kg/gün dozunda akrilamid ilave edilmiştir. Akrilamid uygulaması sonrası hayvanlar anestezi altında servikal dislokasyonla öldürülmüş ve serumlarında IMA, TAS, TOS ve albumin düzeyleri spektrofotometrik yöntem ile ölçülmüştür. 2 mg/kg ve 5mg/kg akrilamid verilen erkek sıçanlara ait serum IMA düzeyleri kontrol grubuna göre önemli derecede yüksek bulunmuştur. Ayrıca, 5mg/kg akrilamid verilen erkek sıçanlara ait serum TAS düzeyleri kontrol grubuna göre önemli derecede düşük ve serum TOS değerleri önemli derecede yüksek bulunmuştur. 2 mg/kg ve 5mg/kg akrilamid verilen dişi sıçanlara ve kontrol grubuna ait serum IMA, TAS, TOS ve albumin düzeyleri arasında istatistiki açıdan önemli bir fark bulunamamıştır. Bu sonuçlara bağlı olarak bulgularımız, akrilamidin oksidatif stresi artırdığını göstermektedir.The aim of this study was to investigate serum total antioxidant status (TAS), total oxidant status (TOS) and ischemia-modified albumin (IMA) levels in long term acrylamide (ACR) given rats, compared to control rats. In total, 25 male and 25 female Wistar rats were involved in this experiment. Animals in each sex were segregated into three groups. Two of them were treatment groups and one of them was control group. Each treatment group consisted of ten animals and each control group consisted of five animals. ACR was administered to the treatment groups at 2 and 5 mg/kg/day via drinking water for 90 days. In the end of the experiment, serum samples were analyzed for IMA, TAS, TOS and albumin levels with the spectrophotometric method. Serum IMA and adjusted IMA levels were significantly higher at concentrations of 2 mg/kg and 5 mg/kg in the male rats when compared with those of the control male rats. Serum TAS levels significantly decreased at concentrations of 5 mg/kg in the male rats when compared with those of the control rats. We also observed a significant increase in the levels of serum TOS at concentrations of 5 mg/kg in the male rats. There were no significant differences between serum IMA, TAS, TOS and albumin levels at concentrations of 2 mg/kg and 5 mg/kg in the female rats. Our findings show that long term treatment with 2 mg/kg and 5 mg/kg doses of ACR led to a significant depletion of serum TAS levels and overproduction of serum TOS and IMA levels, consequently, to an increase in oxidative stress