• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.861-867
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• 2006

The objective of this study is to analyze the landslide hazard areas by combining LRA (Lgistic Regression Analysis) and AHP (Analytic Hierarchy Program) methods with Remote Sensing and GIS data in Anseong-si. In order to classify landslide hazard areas of seven levels, six topographic factors (slope, aspect, elevation, soil drain, soil depth, and land use) were used as input factors of LRA and AHP methods. As results, high-risk areas for landslide (1 and 2 levels) by LRA and AHP of its own were classified as 46.1% and 48.7%, respectively. A new method by applying weighting factors to the results of LRA and AHP was suggested. High-risk areas for landslide (1 and 2 levels) form the new method was classified as 58.9%.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.25-46
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• 2020

This paper presents ordinal probit semiparametric regression models using Bayesian Spectral Analysis Regression (BSAR) method. Ordinal probit regression is a way of modeling ordinal responses - usually more than two categories - by connecting the probability of falling into each category explained by a combination of available covariates using a probit (an inverse function of normal cumulative distribution function) link. The Bayesian probit model facilitates posterior sampling by bringing a latent variable following normal distribution, therefore, the responses are categorized by the cut-off points according to values of latent variables. In this paper, we extend the latent variable approach to a semiparametric model for the Bayesian ordinal probit regression with nonparametric functions using a spectral representation of Gaussian processes based BSAR method. The latent variable is decomposed into a parametric component and a nonparametric component with or without a shape constraint for modeling ordinal responses and predicting outcomes more flexibly. We illustrate the proposed methods with simulation studies in comparison with existing methods and real data analysis applied to a Korean National Health and Nutrition Examination Survey (KNHANES) 2016 for investigating nonparametric relationship between smoking behavior and coffee intake.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.5
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• pp.387-392
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• 2017

There is a constant demand for hull variation related to ship design. Various input variables are generally given to achieve the objective functions assigned by each variation process. When dealing with geometric shapes accompanied by nonlinear operations during the variation process, vague relationships or uncertainties among input variables are commonly observed. Therefore, it is strongly recommended to identify those uncertainty factors in advance. A method to modify the shape of a closed hull form with a new area and a centroid had been introduced as a new process of hull variation. Since uncertainty between input variables still existed in the method, however, it was not easy for the user to enter the area and the corresponding centroid. To overcome this problem, a method is presented in this paper to provide the feasible region of centroids for a given area. By utilizing the concept and techniques used in the statistics such as the number of samples, probability, margin error, and level of confidence, this method generates the distribution of possible centroids along the regression curve. The result shows that the method helps the user to choose an appropriate input value following his or her design intention.

• Journal of Agricultural Extension & Community Development
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• v.15 no.4
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• pp.577-597
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• 2008

The purpose of this study is to examine the effects of residents' perceptions of tourism impact and conflicts on residents' participation in rural tourism village. Method of analysis involves factor analysis and regression analysis in this study. In order to measure the level of perception, three factors(economic benefits, social benefits, social and environmental cost) are derived from the factor analysis. And also in order to measure the level of conflict, two factor(openness of information, leading of operation) are derive from the factor analysis. The result of regression analysis indicate that perceived economic benefits and social benefits are rather greater impacts on residents' participation than social and environmental cost, and also openness of information is rather greater impacts on residents' participation than leading of operation.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.266-272
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• 2005

This study is to empirically analyze the effects on job performance of Accounting Information Systems, Analysis methods were used to Cronbach's Alpha analysis, Factor analysis, analysis of variance(ANOVA) and regression in odor by the contingency grouping method. The results of this study are as follows : First, The regression analysis had effects on AIS utility and job performance. Second, The Analysis of variance(ANOVA) had non-effects on systems operating degree and systems satisfaction. Third, The input variables of information accuracy and systems satisfaction had additional effect about IT capability.

• East Asian Journal of Business Economics (EAJBE)
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• v.9 no.1
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• pp.17-26
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• 2021

Purpose-This study examines how financial slack resources and subcontracting of small and medium-sized enterprises (SMEs) affect their internationalization. To identify slack resources, subcontracting, and internationalization of SMEs, 1,062 SME samples in the electronics industry are used in the logistic regression analysis to analyze their relationship with SMEs' export. Research design, data, and methodology-This study conducted the empirical analysis on 1,062 SMEs in the electronics industry using the sample survey method. The samples were based on data selected and distributed by the Ministry SMEs and Startups. The data analysis methods were descriptive, correlation analysis, and logistics regression analysis. Result-The analysis shows that only available resources are negatively related to SMEs' internationalization. It can be interpreted as a high tendency for SMEs to avoid relatively risky choices such as entering overseas markets if they have enough financial resources. Moreover, subcontracting has a negative relationship with internationalization. Conclusion-This study broadened the scope of SME research by analyzing subcontracting and slack resources together and provides practical implications for policymakers and managers.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.329-334
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• 2004

The objective of this study regards the preceding condition of the construction disposal of waste which is appropriate, with occurrence quantity DB anger the occurrence quantity prediction which is accurate the regression model which it sees and with the method which is mote accurate prediction method of existing than to sleep it presents it does. This study acquires apartment results data of public construction and civil construction, and chose factor that exert biggest influence on the waste occurrence amount through question and interview memorial address by regression model variable. And presented regression mode] which uses statistics program named SPSS. Result of this study by regression model through constant results data DB anger existent error big experience than estimate method that corrector estimation is available show.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.217-227
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• 2022

Quantile regression is widely used in many fields based on the advantage of providing an efficient tool for examining complex information latent in variables. However, modern large-scale and high-dimensional data makes it very difficult to estimate the quantile regression model due to limitations in terms of computation time and storage space. Divide-and-conquer is a technique that divide the entire data into several sub-datasets that are easy to calculate and then reconstruct the estimates of the entire data using only the summary statistics in each sub-datasets. In this paper, we studied on a variable selection method using Bayes information criteria by applying the divide-and-conquer technique to the penalized quantile regression. When the number of sub-datasets is properly selected, the proposed method is efficient in terms of computational speed, providing consistent results in terms of variable selection as long as classical quantile regression estimates calculated with the entire data. The advantages of the proposed method were confirmed through simulation data and real data analysis.

• Journal of Information Processing Systems
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• v.12 no.1
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• pp.109-128
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• 2016

This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.5
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• pp.140-150
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• 2003

Observational methods such as the Asaoka's method and the hyperbolic method are widely applied on the settlement analysis using observed settlement. The most unreliable aspects in those methods is arose from the subjective discretion of initial non-linearity on linear regression. The initial non-linearity is inevitable due to the settlement behaviour itself. Therefore an objective method is essential to achieve more reliable results on settlement analysis. It was found that the initial non-linear data are statistical outliers. New automation algorithms of the hyperbolic and the Asaoka's method were developed based on outlier detection method. The methods are a successive detection of outliers and a searching method of suitable hyperbolic range for the Asaoka's and the hyperbolic method respectively. Applicability of the algorithms was verified through case studies.