• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• Structural Engineering and Mechanics
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• v.54 no.1
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• pp.87-103
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• 2015

This paper focuses on an analytical research on the critical buckling load of cylindrical shells with stepwise variable wall thickness under axial compression. An arctan function is established to describe the thickness variation along the axial direction of this kind of cylindrical shells accurately. By using the methods of separation of variables, small parameter perturbation and Fourier series expansion, analytical formulas of the critical buckling load of cylindrical shells with arbitrary axisymmetric thickness variation under axial compression are derived. The analysis is based on the thin shell theory. Analytic results show that the critical buckling load of the uniform shell with constant thickness obtained from this paper is identical with the classical solution. Two important cases of thickness variation pattern are also investigated with these analytical formulas and the results coincide well with those obtained from other authors. The cylindrical shells with stepwise variable wall thickness, which are widely used in actual engineering, are studied by this method and the analytical formulas of critical buckling load under axial compression are obtained. Furthermore, an example is presented to illustrate the effects of each strake's length and thickness on the critical buckling load.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• Journal of the Korean housing association
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• v.8 no.2
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• pp.71-84
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• 1997

The Purpose of this study is to estimate how inclination to move can be appeared by understanding the cognition of a resident on stress due to the residential environment. 240 housewives living in Chiniu were Questioned statistical analysis were used with factor analysis, F-test. Duncan's Multiple range analysis, stepwise regression analysis and stepwise discriminant analysis, The result were summarized as follows 1) The stress of residential environment were clissified by six factors indoor facility, educational environmental. indoor structure, air Pollution noise, traffic convenience. 2) The extent of a stress from residential environment was significantly different in the socio-demographic variable and housing-related variable. 3) The stress of residential environment were affected by the direction of house. 4) The variable discriminating inclination to move were the stress of residential environment(air Pollution). an educational level, the type of housing possession, residential Period and the size of house.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.949-952
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• 2002

A regression model is used in predicting the response variable given predictor variables However, in case of large number of predictor variables, a regression model has some problems such as multicollinearity, interpretation of the functional relationship between the response and predictors and prediction accuracy. A clustering method and stepwise regression could be used to reduce the amount of data by grouping predictors having similar properties and by selecting the subset of predictors. respectively. This paper proposes a prediction method combining clustering method and stepwise regression. The proposed method fits a global model and local models and predicts responses given new observations by using both models. The paper also compares the performance of proposed method with stepwise regression via a real data of ample obtained in a steel process.

• Journal of Drive and Control
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• v.15 no.2
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• pp.9-21
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• 2018

The aim of this study was to develop and test a model of an auto-sensing hydraulic breaker that can automatically change its 4-step impact mode according to the rock strength using SimulationX. The auto-sensing hydraulic breaker with a 4-step variable impact mode has the advantage of obtaining optimal impact energy and impact frequency under various rock conditions compared to an auto-sensing hydraulic breaker with a 2-step variable impact mode, which has already been developed overseas. Several steps were necessary to conduct this study. First, the operation principle of the auto-sensing hydraulic breaker with the 2-step variable impact mode was analyzed. Based on the findings, an analysis model of the auto-sensing hydraulic breaker with the 4-step variable impact mode was developed (and compared with the 2-step variable impact mode) Finally, an analysis of the results established that the stepwise variable of the impact mode was implemented according to the rock strength and the difference of each impact mode was confirmed. This study is expected to contribute to the development of auto-sensing hydraulic breakers that are superior to those developed by advanced companies in foreign countries.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.3
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• pp.400-409
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• 2011

The purpose of this study was to investigate the influence of irrational beliefs on parenting stress of mothers with early children. For achieving this purpose, Irrational Belief Test and Parenting Stress Index were administered to 300 mothers with early children in Busan and data from 234 mothers were used for statistical analysis. Collected data were analyzed using Pearson correlation coefficient and stepwise multiple regression analysis. The results of correlational analysis showed that irrational beliefs were positively related to parenting stress. Anxious over-concern factor was related to all parenting stress variables. The results of stepwise regression analysis revealed that 2~4 irrational beliefs significantly influenced parenting stress; sub-factors of parenting stress variable that irrational beliefs had the most effect on was competence factor. Based upon these results, it can be concluded that irrational belief is an important variable which predicts parenting stress of mothers with early children.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.513-521
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• 2010

Most of universities have carried out course evaluation to apply the performance appraisal for professor. But, course evaluation depends on characteristics of each class such as class size, type of lecture, evaluator's grade and so on. As the results, such characteristics of each class lead to serious bias which makes lecturers distrust the course evaluation results. Hence, we propose a equating method for the course evaluation by regression analysis which use stepwise variable selection. And we compare proposed method with the other method by Cho et al. (2009) with respect to efficiencies. Also we give the example to which the method is applied.

• Journal of KIISE:Software and Applications
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• v.41 no.8
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• pp.598-604
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• 2014

In this paper, we develop stepwise regression data envelopment model to select important variables. We formulate null hypothesis to understand the importance of each variable and use Kruskal-Wallis test for this purpose. If the Kruskal-Wallis test does reject the null hypothesis this will imply there is significant fluctuation in the efficiency score relative to base model. And therefore we have to further check the pair of variables that causes the fluctuation in order to determine its importance using Conover-Inman test. The proposed models helps understand the extent of misclassification decision making units as efficient/inefficient when variables are retained or discarded alongside provides useful managerial prescription to make improvement strategies.