Mehmet Ali Cengiz

Variable selection in gamma regression models via artificial bee colony algorithm

ABSTRACT Variable selection is an important task in regression analysis. Performance of the statistical model highly depends on the determination of the subset of predictors. There are several methods to select most relevant variables to construct a good model. However in practice, the dependent variable may have positive continuous values and not normally distributed. In such situations, gamma distribution is more suitable than normal for building a regression model. This paper introduces an heuristic approach to perform variable selection using artificial bee colony optimization for gamma regression models. We evaluated the proposed method against with classical selection methods such as backward and stepwise. Both simulation studies and real data set examples proved the accuracy of our selection procedure.

Energy performance evaluation of OECD countries using Bayesian stochastic frontier analysis and Bayesian network classifiers

ABSTRACT More recently a large amount of interest has been devoted to the use of Bayesian methods for deriving parameter estimates of the stochastic frontier analysis. Bayesian stochastic frontier analysis (BSFA) seems to be a useful method to assess the efficiency in energy sector. However, BSFA results do not expose the multiple relationships between input and output variables and energy efficiency. This study proposes a framework to make inferences about BSFA efficiencies, recognizing the underlying relationships between variables and efficiency, using Bayesian network (BN) approach. BN classifiers are proposed as a method to analyze the results obtained from BSFA.

Particle swarm optimization-based variable selection in Poisson regression analysis via information complexity-type criteria

ABSTRACT Modeling of count responses is widely performed via Poisson regression models. This paper covers the problem of variable selection in Poisson regression analysis. The basic emphasis of this paper is to present the usefulness of information complexity-based criteria for Poisson regression. Particle swarm optimization (PSO) algorithm was adopted to minimize the information criteria. A real dataset example and two simulation studies were conducted for highly collinear and lowly correlated datasets. Results demonstrate the capability of information complexity-type criteria. According to the results, information complexity-type criteria can be effectively used instead of classical criteria in count data modeling via the PSO algorithm.

Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm

ABSTRACT In statistical analysis, one of the most important subjects is to select relevant exploratory variables that perfectly explain the dependent variable. Variable selection methods are usually performed within regression analysis. Variable selection is implemented so as to minimize the information criteria (IC) in regression models. Information criteria directly affect the power of prediction and the estimation of selected models. There are numerous information criteria in literature such as Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). These criteria are modified for to improve the performance of the selected models. BIC is extended with alternative modifications towards the usage of prior and information matrix. Information matrix-based BIC (IBIC) and scaled unit information prior BIC (SPBIC) are efficient criteria for this modification. In this article, we proposed a combination to perform variable selection via differential evolution (DE) algorithm for minimizing IBIC and SPBIC in linear regression analysis. We concluded that these alternative criteria are very useful for variable selection. We also illustrated the efficiency of this combination with various simulation and application studies.

Subset selection in quantile regression analysis via alternative Bayesian information criteria and heuristic optimization

ABSTRACT Subset selection is an extensively studied problem in statistical learning. Especially it becomes popular for regression analysis. This problem has considerable attention for generalized linear models as well as other types of regression methods. Quantile regression is one of the most used types of regression method. In this article, we consider subset selection problem for quantile regression analysis with adopting some recent Bayesian information criteria. We also utilized heuristic optimization during selection process. Simulation and real data application results demonstrate the capability of the mentioned information criteria. According to results, these information criteria can determine the true models effectively in quantile regression models.

Graphical modeling for item difficulty in medical faculty exams

Background: There are different indexes used in the evaluation of exam results. One important index is the difficulty level of the item that is also used in this study to obtain control charts. This article offers some suggestions for the improvement of multiple-choice tests using item analysis statistics. Materials and Methods: The graphical modeling is important for the rapid and comparative evaluation of test results. The control chart is a tool that can be used to sharpen our teaching and testing skills by inspecting the weaknesses of measurements and producing reliable items. The research data for the application of control charts were obtained using the results of the fourth and fifth-grade students exams at Ondokuz Mayis University, Faculty of Medicine. I-chart or moving range chart (MR) is preferred for whole variable data. Results: It is seen that all observations are within control limits for I-chart, but three points on MR-chart are settled on the LCL. Using X–-chart with subgroups, it was determined that control measurements were within the upper and lower limits in both charts. The difficulty levels of items were examined by obtaining different variable control charts. The difficulty level of the two items exceeded the upper control limit in R- and S-charts. Conclusion: The control charts have the advantage for classifying items as acceptable or unacceptable based on item difficulty criteria.

Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements

We investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from sedation measurement for Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT). Data for each patient is observed at different time points within the time up to 60 min. A model for the sedation level of patients is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response, and then subsequent terms are introduced. To estimate the model, we use the Gibbs sampling given some appropriate prior distributions.

An application of multinomial response models using bayesian approach

Abstract This study focuses on a specific case of Generalized Linear Models for multinomial responses and investigates their analysis using Bayesian methods. The important problem of surgical planning for arterial occlusive disease is considered in some detail, as a medical application of these models. The study investigates the potential benefits which accrue when these ideas are applied to the important medical problem of locating and assessing the severity of lower-limb arterial occlusive disease.

ÇOKLU DOĞRUSAL REGRESYONDA MODEL SEÇİMİNDE GENELLEŞTİRİLMİŞ TOPLAMSAL MODELLERİN KULLANIMI

Coklu dogrusal regresyon yaygin olarak kullanilan istatistiksel yontemlerden birisidir. Varsayimlar saglandiginda oldukca guclu bir aractir. Bu varsayimlardan biri de bagimli degiskenler ile aciklayici degiskenler arasindaki iliskinin dogrusal, polinomial veya ustel gibi bir bilinen matematiksel fonksiyona sahip olmasidir. Ancak cogu uygulamalarda tanimli boyle bir fonksiyon bulunamayabilir veya bu iliski kolayca tanimlanamayabilir. Genellestirilmis Toplamsal Modeller (GAM), var olan iliskileri ortaya cikarmak icin tanimli bir fonksiyonla, parametrik olmayan bir duzlestiriciyi yer degistirerek bu varsayimi esnetir. GAM coklu regresyonda model seciminde de kullanilabilir. Bu calisma da, coklu dogrusal regresyon da model seciminde GAM’in kullanimi uzerine odaklanilmaktadir.

Damar Tıkanıklığı Tespitinde Bayesci Bir Yaklaşım

Damar tikanikligi bir damarin kismi olarak tikanmasiyla olusan, dolayisiyla kan akisinin azalmasina ve siddetli agriya neden olan bir rahatsizliktir. Bu calismada; cerrahi mudahale oncesi yardimda bulunmak amaciyla damar tikanikligi tespitinde iki degiskenli genellestirilmis lineer modeller Bayesci bir yaklasimla kullanilmistir A Bayesian Approach for Assessing Arterial Occlusive Disease Arterial Occlusive disease is a severe ailment, which consists of the partial blockage (stenoses) of an artery, leading to a diminished flow of blood and corresponding pain. In this study, bivariate generalized linear models with Bayesian approach were used for assessing arterial occlusive disease.