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Öğe Constitution of random intercept and slope model (RISM) as a special case of linear mixed models (LMMs) for repeated measurements data(ELSEVIER SCIENCE INC, 2011) Iyit, Neslihan; Genc, AsirIn this paper, for the aim of modeling variance-covariance structure matrix of the response variables vector in random intercept and slope model (RISM) from linear mixed models (LMMs) for repeated measurements data, 13 different homogeneous and heterogeneous variance-covariance structure models are investigated comparatively in an application from a clinical trial. (C) 2011 Elsevier Inc. All rights reserved.Öğe An evaluation of the operational efficiency of turkish airports using data envelopment analysis and the Malmquist productivity index: 2009-2014 case(ELSEVIER SCI LTD, 2016) Orkcu, H. Hasan; Balikci, Cemal; Dogan, Mustafa Isa; Genc, AsirTurkey's airport industry has experienced substantial growth over the recent years, but few studies have analysed their operational efficiency. This paper uses Malmquist productivity index (classical and bootstrapping) to assess the operational performance of 21 Turkey airports during the period of 2009 through 2014. The findings indicated that the efficiency and productivity of the majority of the Turkish airports increased during the period under investigation. However, in the period of 2011-2012, a significant decline was observed in efficiency. The main reason of this stagnation is the significant increase in the physical capacity of the Turkish airports in 2011. The non-reflection of the increasing physical capacity to passenger and cargo traffic caused a decline in 2012. In spite of declining in the period of 2011-2012, efficiency values of Turkish airports have increased again since 2013. Moreover, decomposition of the Malmquist index showed that most Turkey airports experienced losses in efficiency; however, in terms of technology, they have progressed. Two significant factors (i.e. operating hours and percentage of international traffic) were identified by the Simar-Wilson double bootstrapping regression analysis as explaining variations in airport efficiency. (C) 2016 Published by Elsevier Ltd.Öğe Modified ridge regression parameters: A comparative Monte Carlo study(HACETTEPE UNIV, FAC SCI, 2014) Asar, Yasin; Karaibrahimoglu, Adnan; Genc, AsirIn multiple regression analysis, the independent variables should be uncorrelated within each other. If they are highly intercorrelated, this serious problem is called multicollinearity. There are several methods to get rid of this problem and one of the most famous one is the ridge regression. In this paper, we will propose some modified ridge parameters. We will compare our estimators with some estimators proposed earlier according to mean squared error (MSE) criterion. All results are calculated by a Monte Carlo simulation. According to simulation study, our estimators perform better than the others in most of the situations in the sense of MSE.Öğe New Shrinkage Parameters for the Liu-type Logistic Estimators(TAYLOR & FRANCIS INC, 2016) Asar, Yasin; Genc, AsirThe binary logistic regression is a widely used statistical method when the dependent variable has two categories. In most of the situations of logistic regression, independent variables are collinear which is called the multicollinearity problem. It is known that multicollinearity affects the variance of maximum likelihood estimator (MLE) negatively. Therefore, this article introduces new shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) in order to decrease the variance and overcome the problem of multicollinearity. A Monte Carlo study is designed to show the goodness of the proposed estimators over MLE in the sense of mean squared error (MSE) and mean absolute error (MAE). Moreover, a real data case is given to demonstrate the advantages of the new shrinkage parameters.Öğe A New Two-Parameter Estimator for the Poisson Regression Model(SPRINGER INTERNATIONAL PUBLISHING AG, 2018) Asar, Yasin; Genc, AsirIt is known that multicollinearity affects the maximum likelihood estimator (MLE) negatively when estimating the coefficients in Poisson regression. Namely, the variance of MLE inflates and the estimations become instable. Therefore, in this article we propose a new two-parameter estimator (TPE) and some methods to estimate these two parameters for the Poisson regression model when there is multicollinearity problem. Moreover, we conduct a Monte Carlo simulation to evaluate the performance of the estimators using mean squared error (MSE) criterion. We finally consider a real data application. The simulations results show that TPE outperforms MLE in almost all the situations considered in the simulation and it has a smaller MSE and smaller standard errors than MLE in the application.Öğe A New Wavelet Regression Based Approach That Calculates Portfolio Risk(Selçuk Üniversitesi, 2013) Yilmaz, Tarik; Genc, AsirFor investors, one of the primary methods to reduce their risks is to create a portfolio by diversifying their investments. In studies conducted on portfolio management, a relation between the highest income and the lowest risk is aimed at. This is because investors tend to avoid risk and chose minimum level of risk at a given level of income, while choosing the highest level of income at a given risk level. In this case, accurate calculation of portfolio risk is crucial in terms of not misleading investors. In recent years, the statistically based Value at Risk (VaR) method has started to be widely used in determining market risk. VaR summarizes the maximum (worst) loss, at aimed time and at a given level of confidence. In the present study a new VaR calculation approach was proposed by using Wavelet regression. Effectiveness of this new method was tried to be demonstrated with an exemplary case.Öğe A note on some new modifications of ridge estimators(ACADEMIC PUBLICATION COUNCIL, 2017) Asar, Yasin; Genc, AsirRidge estimator is an alternative to ordinary least square estimator, when there is multicollinearity problem. There are many proposed estimators in literature. In this paper, we propose some new estimators. A Monte Carlo experiment has been conducted for the comparison of the performances of the estimators. Mean squared error (MSE) is used as a performance criterion. The benefits of new estimators are illustrated using a real dataset. According to both simulation results and application, our new estimators have better performances in the sense of MSE in most of the situations.Öğe Parameter Estimations via Genetic Algorithm in Multiresponse Nonlinear Models(Selçuk Üniversitesi, 2013) Karakoca, Aydin; Genc, AsirMulti-response non-linear models have been used for modelling functional relationship between dependent and independent variable(s) in most of applications. Parameters of multi-response non-linear models can be estimated by least squares (LS) method. Gauss-Newton, Levenberg-Marquardt and Steepest Descent are most widely used algorithms in LS method. These algorithms requires the condition that the function of independent variables can be differentiable at least two times. Also these algorithms have a risk of unreachable solution which depends according to the chosen starting point. In this study as an alternative to these algorithms, genetic algorithm have recommended for parameter estimation in multi-response non-linear models. And then the parameter estimation results of multi-response nonlinear model that obtained by least squares method and genetic algorithm were compared.Öğe A study on university stundents’ perception of service quality with servqual measurement model(2012) Caglıyan, Vural; Genc, Asir; Yılmaz, TarikUsing SERVQUAL (SERVice QUALity) measurement model, this study aims to examine the relations between expectation and perception of university students at Department of Statistics, Faculty of Science Selçuk University with regard to quality of the services university offer to them. SERVQUAL measurement model is a method which is frequently used in literature to measure service quality and the validation of the scale used in this method have been determined in many previous studies. SERVQUAL measurement model and perception of service quality will be examined by Structural Equation Modeling (SEM) and the results will be compared to the results of the previous studies in literature. This study was also undertaken to lay basis for further studies which are going to carried out in different departments at Selçuk University.Öğe Two-parameter ridge estimator in the binary logistic regression(TAYLOR & FRANCIS INC, 2017) Asar, Yasin; Genc, AsirThe binary logistic regression is a commonly used statistical method when the outcome variable is dichotomous or binary. The explanatory variables are correlated in some situations of the logit model. This problem is called multicollinearity. It is known that the variance of the maximum likelihood estimator (MLE) is inflated in the presence of multicollinearity. Therefore, in this study, we define a new two-parameter ridge estimator for the logistic regression model to decrease the variance and overcome multicollinearity problem. We compare the new estimator to the other well-known estimators by studying their mean squared error (MSE) properties. Moreover, a Monte Carlo simulation is designed to evaluate the performances of the estimators. Finally, a real data application is illustrated to show the applicability of the new method. According to the results of the simulation and real application, the new estimator outperforms the other estimators for all of the situations considered.
