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Öğe Big data analytics: integrating penalty strategies(Taylor & Francis Ltd, 2016) Ahmed, S. Ejaz; Yuzbasi, BahadirWe present efficient estimation and prediction strategies for the classical multiple regression model when the dimensions of the parameters are larger than the number of observations. These strategies are motivated by penalty estimation and Stein-type estimation procedures. More specifically, we consider the estimation of regression parameters in sparse linear models when some of the predictors may have a very weak influence on the response of interest. In a high-dimensional situation, a number of existing variable selection techniques exists. However, they yield different subset models and may have different numbers of predictors. Generally speaking, the least absolute shrinkage and selection operator (Lasso) approach produces an over-fitted model compared with its competitors, namely the smoothly clipped absolute deviation (SCAD) method and adaptive Lasso (aLasso). Thus, prediction based only on a submodel selected by such methods will be subject to selection bias. In order to minimize the inherited bias, we suggest combining two models to improve the estimation and prediction performance. In the context of two competing models where one model includes more predictors than the other based on relatively aggressive variable selection strategies, we plan to investigate the relative performance of Stein-type shrinkage and penalty estimators. The shrinkage estimator improves the prediction performance of submodels significantly selected from existing Lasso-type variable selection methods. A Monte Carlo simulation study is carried out using the relative mean squared error (RMSE) criterion to appraise the performance of the listed estimators. The proposed strategy is applied to the analysis of several real high-dimensional data sets.Öğe CHOICE OF SMOOTHING PARAMETER FOR KERNEL TYPE RIDGE ESTIMATORS IN SEMIPARAMETRIC REGRESSION MODELS(Inst Nacional Estatistica-Ine, 2021) Yilmaz, Ersin; Yuzbasi, Bahadir; Aydin, DursunThis paper concerns kernel-type ridge estimators of parameters in a semiparametric model. These estimators are a generalization of the well-known Speckman's approach based on kernel smoothing method. The most important factor in achieving this smoothing method is the selection of the smoothing parameter. In the literature, many selection criteria for comparing regression models have been produced. We will focus on six selection criterion improved version of Akaike information criterion (AIC(c)), generalized cross-validation (GCV), Mallows' C-p criterion, risk estimation using classical pilots (RECP), Bayes information criterion (BIC), and restricted maximum likelihood (REML). Real and simulated data sets are considered to illustrate the key ideas in the paper. Thus, suitable selection criterion are provided for optimum smoothing parameter selection.Öğe Confidence in Government and Attitudes toward Bribery: A Country-Cluster Analysis of Demographic and Religiosity Perspectives(Mdpi, 2017) Benk, Serkan; Yuzbasi, Bahadir; Mcgee, Robert W.In this study, we try to classify the countries by the levels of confidence in government and attitudes toward accepting bribery by using the data of the sixth wave (2010-2014) of the World Values Survey (WVS). We are also interested in which demographic, attitudinal, and religiosity variables affect each class of countries. For these purposes cluster analysis, linear regression analysis, and ordered logistic regression analysis were used. The study found that countries could be grouped into two clusters which had varying levels of opposition to bribe taking and confidence in government. Another finding was that certain demographic, attitudinal, and religiosity variables that were significant in one cluster might not be significant in another cluster.Öğe Does Religiosity Affect Attitudes toward the Ethics of Tax Evasion? The Case of Turkey(Mdpi, 2020) McGee, Robert W.; Benk, Serkan; Yuzbasi, Bahadir; Budak, TamerThis study surveys the opinion of a wide segment of Turkish society on the ethics of tax evasion. The survey instrument includes 18 statements used to justify tax evasion in the past. The research also finds that some reasons to justify tax evasion proved more attractive to participants than others. In our survey, the strongest support for tax evasion was in cases where the government abused human rights, where the government was corrupt or wasted tax funds, or where the taxpayer did not benefit from the tax expenditures. Conversely, the weakest arguments were in cases where the taxpayer did benefit from the tax expenditures or where the tax funds were spent wisely. What separates this study from others on the ethics of tax evasion is that it addresses interpersonal and intrapersonal religiosity. Its finding confirms the existence of an important relationship between both interpersonal and intrapersonal religiosity and the view toward the ethics of tax evasion.Öğe Government expenditures and trade deficits in Turkey: Time domain and frequency domain analyses(Elsevier Science Bv, 2013) Kayhan, Selim; Bayat, Tayfur; Yuzbasi, BahadirThe purpose of this study is to determine the causality between trade deficit and government expenditures in the Turkish economy. We employ bootstrap process-based Toda-Yamamoto causality and frequency domain analysis methods. Results obtained from both methods imply that there is a bi-directional causality between trade deficits and government expenditures. Different from Toda-Yamamoto causality analysis, frequency domain causality analysis indicates that the causality running from government expenditures to trade deficits exists in the short and medium terms while causality runs from trade deficits to government expenditures in the short and long runs. (C) 2013 Elsevier B.V. All rights reserved.Öğe High Dimensional Data Analysis: Integrating Submodels(Springer International Publishing Ag, 2017) Ahmed, Syed Ejaz; Yuzbasi, BahadirWe consider an efficient prediction in sparse high dimensional data. In high dimensional data settings where d >> n, many penalized regularization strategies are suggested for simultaneous variable selection and estimation. However, different strategies yield a different submodel with d(i) < n, where di represents the number of predictors included in ith submodel. Some procedures may select a submodel with a larger number of predictors than others. Due to the trade-off between model complexity and model prediction accuracy, the statistical inference of model selection becomes extremely important and challenging in high dimensional data analysis. For this reason we suggest shrinkage and pretest strategies to improve the prediction performance of two selected submodels. Such a pretest and shrinkage strategy is constructed by shrinking an overfitted model estimator in the direction of an underfitted model estimator. The numerical studies indicate that our post-selection pretest and shrinkage strategy improved the prediction performance of selected submodels.Öğe High-Dimensional Regression Under Correlated Design: An Extensive Simulation Study(Springer International Publishing Ag, 2019) Ahmed, S. Ejaz; Kim, Hwanwoo; Yildirim, Gokhan; Yuzbasi, BahadirRegression problems where the number of predictors, p, exceeds the number of responses, n, have become increasingly important in many diverse fields in the last couple of decades. In the classical case of small p and large n, the least squares estimator is a practical and effective tool for estimating the model parameters. However, in this so-called Big Data era, models have the characteristic that p is much larger than n. Statisticians have developed a number of regression techniques for dealing with such problems, such as the Lasso by Tibshirani (J R Stat Soc Ser B Stat Methodol 58:267-288, 1996), the SCAD by Fan and Li (J Am Stat Assoc 96(456):1348- 1360, 2001), the LARS algorithm by Efron et al. (Ann Stat 32(2):407-499, 2004), the MCP estimator by Zhang (Ann Stat. 38:894-942, 2010), and a tuning-free regression algorithm by Chatterjee (High dimensional regression and matrix estimation without tuning parameters, 2015, https://arxiv.org/abs/1510.07294). In this paper, we investigate the relative performances of some of these methods for parameter estimation and variable selection through analyzing real and synthetic data sets. By an extensive Monte Carlo simulation study, we also compare the relative performance of proposed methods under correlated design matrix.Öğe HOW RELIGIONS AFFECT ATTITUDES TOWARD ETHICS OF TAX EVASION? A COMPARATIVE AND DEMOGRAPHIC ANALYSIS(Univ Babes-Bolyai, 2015) Benk, Serkan; McGee, Robert W.; Yuzbasi, BahadirThis paper focuses specifically on how religions shape attitudes towards ethics of tax evasion. Firstly, the paper begins with an overview of the four views on the ethics of tax evasion that have emerged over the centuries, then goes on to review some of the theoretical and empirical literature on the subject. The empirical part of the study examines attitudes toward tax evasion in 57 countries from the perspectives of six religions using the data from Wave 6 (2010-2014) of the World Values Survey. The sample population is more than 52,000. More than a dozen demographic variables were examined. The results study found that attitude toward cheating on taxes does differ by religion.Öğe The Impact of Religiosity on Tax Compliance among Turkish Self-Employed Taxpayers(Mdpi, 2016) Benk, Serkan; Budak, Tamer; Yuzbasi, Bahadir; Mohdali, RaihanaThe aim of this study is to explore the impact between religiosity and voluntary tax compliance and enforced tax compliance for self-employed taxpayers in Turkey, where Islam is the predominant religion. A questionnaire survey was administrated to 375 male and 28 female self-employed taxpayers. In this paper, two dimensions of religiosity, namely interpersonal and intrapersonal religiosity, were studied. Factor analysis and ordinary least squares regression methods were used for data analyses. The results of the study illustrate that general religiosity has a statistically positive impact on both voluntary and enforced tax compliance. When we consider the dimensions of religiosity, only intrapersonal religiosity appears to be a significant contributor only to voluntary tax compliance. Nevertheless, interpersonal religiosity has no significant statistical effect on both voluntary and enforced tax compliance. This is one of the pioneer studies of its kind, and investigates the relationship between religiosity and tax compliance from the perspective of developing countries, particularly, Turkey.Öğe . IMPROVED PENALTY STRATEGIES in LINEAR REGRESSION MODELS(Inst Nacional Estatistica-Ine, 2017) Yuzbasi, Bahadir; Ahmed, S. Ejaz; Gungor, MehmetWe suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators. Further, a Monte Carlo simulation study is conducted to assess the relative performance of the listed estimators. Also, we numerically compare their performance with Lasso, adaptive Lasso and SCAD strategies. Finally, a real data example is presented to illustrate the usefulness of the suggested methods.Öğe IMPROVING ESTIMATIONS IN QUANTILE REGRESSION MODEL WITH AUTOREGRESSIVE ERRORS(Vinca Inst Nuclear Sci, 2018) Yuzbasi, Bahadir; Asar, Yasin; Sik, M. Samil; Demiralp, AhmetAn important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.Öğe L1 Correlation-Based Penalty in High-Dimensional Quantile Regression(Ieee, 2018) Yuzbasi, Bahadir; Ahmed, S. Ejaz; Asar, YasinIn this study, we propose a new method called L1 norm correlation based estimation in quantile regression in high-dimensional sparse models where the number of explanatory variables is large, may be larger than the number of observations, however, only some small subset of the predictive variables are important in explaining the dependent variable. Therefore, the importance of new method is that it addresses both grouping affect and variable selection. Monte Carlo simulations confirm that the new method compares well to the other existing regularization methods.Öğe LAD, LASSO and Related Strategies in Regression Models(Springer International Publishing Ag, 2020) Yuzbasi, Bahadir; Ahmed, Syed Ejaz; Arashi, Mohammad; Norouzirad, MinaIn the context of linear regression models, it is well-known that the ordinary least squares estimator is very sensitive to outliers whereas the least absolute deviations (LAD) is an alternative method to estimate the known regression coefficients. Selecting significant variables is very important; however, by choosing these variables some information may be sacrificed. To prevent this, in our proposal, we can combine the full model estimates toward the candidate sub-model, resulting in improved estimators in risk sense. In this article, we consider shrinkage estimators in a sparse linear regression model and study their relative asymptotic properties. Advantages of the proposed estimators over the usual LAD estimator are demonstrated through a Monte Carlo simulation as well as a real data example.Öğe Liu-type shrinkage estimations in linear models(Taylor & Francis Ltd, 2022) Yuzbasi, Bahadir; Asar, Yasin; Ahmed, S. EjazIn this study, we present the preliminary test, Stein-type and positive part Stein-type Liu estimators in the linear models when the parameter vector beta is partitioned into two parts, namely, the main effects beta(1) and the nuisance effects beta(2) such that beta = (beta(1), beta(2)). We consider the case that a priori known or suspected set of the explanatory variables do not contribute to predict the response so that a sub-model maybe enough for this purpose. Thus, the main interest is to estimate beta(1) when beta(2) is close to zero. Therefore, we investigate the performance of the suggested estimators asymptotically and via a Monte Carlo simulation study. Moreover, we present a real data example to evaluate the relative efficiency of the suggested estimators, where we demonstrate the superiority of the proposed estimators.Öğe On Order Statistics from Nonidentical Discrete Random Variables(De Gruyter Open Ltd, 2016) Yuzbasi, Bahadir; Bulut, Yunus; Gungor, MehmetIn this study, pf and df of single order statistic of nonidentical discrete random variables are obtained. These functions are also expressed in integral form. Finally, pf and df of extreme of order statistics of random variables for the nonidentical discrete case are given.Öğe Penalized regression via the restricted bridge estimator(Springer, 2021) Yuzbasi, Bahadir; Arashi, Mohammad; Akdeniz, FikriThis article is concerned with the bridge regression, which is a special family in penalized regression with penalty function Sigma(p)(j=1) |beta(j) (q) with q > 0, in a linear model with linear restrictions. The proposed restricted bridge (RBRIDGE) estimator simultaneously estimates parameters and selects important variables when a piece of prior information about parameters are available in either low-dimensional or high-dimensional case. Using local quadratic approximation, we approximate the penalty term around a local initial values vector. The RBRIDGE estimator enjoys a closed-form expression that can be solved when q > 0. Special cases of our proposal are the restricted LASSO (q = 1), restricted RIDGE (q = 2), and restricted Elastic Net (1 < q < 2) estimators. We provide some theoretical properties of the RBRIDGE estimator for the low-dimensional case, whereas the computational aspects are given for both low- and high-dimensional cases. An extensive Monte Carlo simulation study is conducted based on different prior pieces of information. The performance of the RBRIDGE estimator is compared with some competitive penalty estimators and the ORACLE. We also consider four real-data examples analysis for comparison sake. The numerical results show that the suggested RBRIDGE estimator outperforms outstandingly when the prior is true or near exact.Öğe Rank-based Liu regression(Springer Heidelberg, 2018) Arashi, Mohammad; Norouzirad, Mina; Ahmed, S. Ejaz; Yuzbasi, BahadirDue to the complicated mathematical and nonlinear nature of ridge regression estimator, Liu (Linear-Unified) estimator has been received much attention as a useful method to overcome the weakness of the least square estimator, in the presence of multicollinearity. In situations where in the linear model, errors are far away from normal or the data contain some outliers, the construction of Liu estimator can be revisited using a rank-based score test, in the line of robust regression. In this paper, we define the Liu-type rank-based and restricted Liu-type rank-based estimators when a sub-space restriction on the parameter of interest holds. Accordingly, some improved estimators are defined and their asymptotic distributional properties are investigated. The conditions of superiority of the proposed estimators for the biasing parameter are given. Some numerical computations support the findings of the paper.Öğe Religion and Ethical Attitudes toward Accepting a Bribe: A Comparative Study(Mdpi, 2015) McGee, Robert W.; Benk, Serkan; Yuzbasi, BahadirThis study presents the results of an empirical study of ethical attitudes toward bribe taking in six religionsChristianity, Islam, Buddhism, the Baha'i faith, Hinduism, and Judaism. The paper begins with a discussion of the theoretical and empirical literature on the subject. The empirical part of the study examines attitudes toward accepting bribes in 57 countries from the perspectives of six religions using the data from Wave 6 (2010-2014) of the World Values Survey. The sample population is more than 52,000. More than a dozen demographic variables were examined. The study found that attitude toward bribe taking does differ by religion.Öğe Ridge Type Shrinkage Estimation of Seemingly Unrelated Regressions And Analytics of Economic and Financial Data from Fragile Five Countries(Mdpi, 2020) Yuzbasi, Bahadir; Ahmed, S. EjazIn this paper, we suggest improved estimation strategies based on preliminarily test and shrinkage principles in a seemingly unrelated regression model when explanatory variables are affected by multicollinearity. To that end, we split the vector regression coefficient of each equation into two parts: one includes the coefficient vector for the main effects, and the other is a vector for nuisance effects, which could be close to zero. Therefore, two competing models per equation of the system regression model are obtained: one includes all the regression of coefficients (full model); the other (sub model) includes only the coefficients of the main effects based on the auxiliary information. The preliminarily test estimation improves the estimation procedure if there is evidence that the vector of nuisance parameters does not provide a useful contribution to the model. The shrinkage estimation method shrinks the full model estimator in the direction of the sub-model estimator. We conduct a Monte Carlo simulation study in order to examine the relative performance of the suggested estimation strategies. More importantly, we apply our methodology based on the preliminarily test and the shrinkage estimations to analyse economic data by investigating the relationship between foreign direct investment and several economic variables in the Fragile Five countries between 1983 and 2018.Öğe Ridge-type pretest and shrinkage estimations in partially linear models(Springer, 2020) Yuzbasi, Bahadir; Ahmed, S. Ejaz; Aydin, DursunIn this paper, we suggest pretest and shrinkage ridge regression estimators for a partially linear regression model, and compare their performance with some penalty estimators. We investigate the asymptotic properties of proposed estimators. We also consider a Monte Carlo simulation comparison, and a real data example is presented to illustrate the usefulness of the suggested methods.
