• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.295-304
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• 2010

Coating thickness is one of target variables in quality control process in steel industry. To predict coating thickness and to control quality of anti-fingerprint steel coils, ultraviolet-visible spectra are measured. We propose a variable-interval selection procedure based on the variable importance in projection in partial least square model. Using the proposed variable interval selection method, prediction performance gets better in the reduced model than the full model with full spectra absorbance. It is also shown that the first differencing as a data preprocessing technique does work well for the prediction of coating thickness.

• Culinary science and hospitality research
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• v.22 no.8
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• pp.109-121
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• 2016

This study analyzed the effect of five sub-variables' attribute of HMR: features of information, diversity, promptness, price and convenience, on the consumer purchasing intention. In addition, the regulation effect of positive reviews and negative reviews of consumers' online reviews between HMR selection attribute and purchasing intention was also tested. Results are following. First, convenience feature (B=.577, p<.001) and diversity feature (B=.093, p<.01) among the effect of HMR selection attribute had a positive (+) effect on purchasing intention. On the other hand, promptness feature (B=.235, p<.001) and price feature (B=.161, p<.001), and information feature (B=.288, p<.001) were not significant effect on purchasing intention. Second, result of regulation effect of the positive reviews of consumer's online review between the selection attribute of the HMR product and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product is input as an independent variable, there was a significant positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In addition, there was significant positive (+) main effect (B=.472, p<.001) in the second step model in which the consumers' positive reviews, that is a regulation variable. Furthermore, the feature of price (B=.068, p<.05) had a significant positive (+) effect in the third stage in which the selection attribute of the HMR product that is an independent variable and the interaction of the positive review. However, the feature of information (B=-.063, p<.05) showed negative (-) effect, and there was no effect on the features of convenience, diversity, and promptness. Third, as a result of testing the regulation effect of the negative reviews of consumers' online reviews between HMR product selection attribute and consumers' purchasing intention, in the first-stage model in which the selection attribute of the HMR product was a positive (+) effect on all the features of convenience, diversity, promptness, price, and information. In the second-stage model in which consumers' negative reviews (B=-.113, p<.001) had negative (-) effect. In the third-stage in which the selection attribute of the HMR product and the interactions of the negative reviews was a positive (+) effect with the feature of price (B=.113, p<.01). Last, there was no effect at all on the features of convenience, promptness, and information.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.646-650
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• 2014

In this paper, in order to avoid the deterioration of the pattern classification performance which results from the curse of dimensionality, we propose a new feature selection method. The newly proposed feature selection method is based on Fuzzy C-Means clustering algorithm which analyzes the data points to divide them into several clusters and the concept of a function with fuzzy numbers. When it comes to the concept of a function where independent variables are fuzzy numbers and a dependent variable is a label of class, a fuzzy number should be related to the only one class label. Therefore, a good feature is a independent variable of a function with fuzzy numbers. Under this assumption, we calculate the goodness of each feature to pattern classification problem. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1079-1094
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• 2014

In this paper, we study efficient gene selection methods by using conditional mutual information. We suggest gene selection methods using conditional mutual information based on semiparametric methods utilizing multivariate normal distribution and Edgeworth approximation. We compare our suggested methods with other methods such as mutual information filter, SVM-RFE, Cai et al. (2009)'s gene selection (MIGS-original) in SVM classification. By these experiments, we show that gene selection methods using conditional mutual information based on semiparametric methods have better performance than mutual information filter. Furthermore, we show that they take far less computing time than Cai et al. (2009)'s gene selection but have similar performance.

• Economic Analysis
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• v.26 no.1
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• pp.84-132
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• 2020

A central bank infers market expectations of future yields based on yield curves. The central bank needs to precisely understand the changes in market expectations of future yields in order to have a more effective monetary policy. This need explains why a range of models have attempted to produce yield curves and market expectations that are as accurate as possible. Alongside the development of bond markets, the interconnectedness between them and macroeconomic factors has deepened, and this has rendered understanding of what macroeconomic variables affect yield curves even more important. However, the existence of various theories about determinants of yields inevitably means that previous studies have applied different macroeconomics variables when estimating yield curves. This indicates model uncertainties and naturally poses a question: Which model better estimates yield curves? Put differently, which variables should be applied to better estimate yield curves? This study employs the Dynamic Nelson-Siegel Model and takes the Bayesian approach to variable selection in order to ensure precision in estimating yield curves and market expectations of future yields. Bayesian variable selection may be an effective estimation method because it is expected to alleviate problems arising from a priori selection of the key variables comprising a model, and because it is a comprehensive approach that efficiently reflects model uncertainties in estimations. A comparison of Bayesian variable selection with the models of previous studies finds that the question of which macroeconomic variables are applied to a model has considerable impact on market expectations of future yields. This shows that model uncertainties exert great influence on the resultant estimates, and that it is reasonable to reflect model uncertainties in the estimation. Those implications are underscored by the superior forecasting performance of Bayesian variable selection models over those models used in previous studies. Therefore, the use of a Bayesian variable selection model is advisable in estimating yield curves and market expectations of yield curves with greater exactitude in consideration of the impact of model uncertainties on the estimation.

• Journal of the Korean Society of Costume
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• v.50 no.5
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• pp.77-90
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• 2000

The purpose of this study is to disclose the dimension of job characteristic, working place situation. and clothes selection, which are variable on clothes selection, to research the relation of job characteristic, working place situation, and clothes selection and to disclose the feature of consumers by each group in accordance with job characteristic. The follows are summary of this study result : The concept structure of job characteristic is composed by four types. Working place situation related with clothes selection are consisted of five types. Professional women selected clothes in accordance with nine feature. Job characteristic is related with working palace situation and influences on clothes selection. The persons influenced by activity state regarded personality as important when select clothes. The persons influenced by duty state regarded harmony and control as important, instead of, they ignored conformity. People select occupation according to individual feature, so the feature of consumers, working place environment, working place situation are different in accordance with job characteristic, as a result we can find differences in clothes selection suitable for working palace and purchasing.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2D
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• pp.193-201
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• 2011

The provision of traffic information plays an important role in increasing social benefit not only by saving travel time for individuals but also by improving the efficiency of road operation. VMS(Variable Message Sign) helps on-wheel drivers easily understand the road situation, and also provides real-time traffic information to people on the streets. However, it has not been sufficiently studied on how traffic information based on VMS influences on the drivers' selection of route. This study investigated how drivers use VMS traffic information and how they are satisfied with it. Then, the model of drivers' route selection was specified with the types of traffic information and the expected travel time to examine the influence on the selection of drivers' route. The model was estimated and analyzed in three types according to the condition of detour roads, and the rate of route change and the degree of sensitivity was calculated from the estimation. The results of analysis are as follows. the $1^{st}$ type model showed the 10% of route change for the travel time saving of 5minutes, and the 81.6% of route change for the travel time saving of 20minutes. The $2^{nd}$ type led to the range of route change from 14.2% to 92.7% over the 5 through 20 minutes of travel time saving. The $3^{rd}$ model resulted in the 99.1% of route change. The sensitivity of route change showed the highest for the travel time saving of 11 minutes with the $1^{st}$ type model, 9 minutes with the $2^{nd}$ type model, and 5 minutes with the $3^{rd}$ type model respectively.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.965-976
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• 2015

Determining relevant variables for a regression model is important in regression analysis. Recently, a variable selection methods using a penalized likelihood with various penalty functions (e.g. LASSO and SCAD) have been widely studied in simple statistical models such as linear models and generalized linear models. The advantage of these methods is that they select important variables and estimate regression coefficients, simultaneously; therefore, they delete insignificant variables by estimating their coefficients as zero. We study how to select proper variables based on penalized hierarchical likelihood (HL) in semi-parametric frailty models that allow three penalty functions, LASSO, SCAD and HL. For the variable selection we develop a new function in the "frailtyHL" R package. Our methods are illustrated with breast cancer survival data from the Medical Center at Chonnam National University in Korea. We compare the results from three variable-selection methods and discuss advantages and disadvantages.

• Journal of the Korean Statistical Society
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• v.20 no.1
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• pp.1-28
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• 1991

Considerable progress on the problem of data-driven bandwidth selection in kernel density estimation has been made recently. The goal of this paper is to provide an introduction to the methods currently available, with discussion at both a practical and a nontechnical theoretical level. The main setting considered here is global bandwidth kernel estimation, but some recent results on variable bandwidth kernel estimation are also included.

• The Korean Journal of Health Service Management
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• v.3 no.1
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• pp.47-58
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• 2009

This study is to grasp selection factors and habits of information searching of customers of ophthalmic service and to verify the differences in them and to investigate how they affect in selecting medical institute by demographic sociological characters, selection factors by classification and habits of information searching, how many times they used and the type of medical treatment. The result of analysis of importance of selection factors of medical institute, it showed that doctors' career were evaluated high by classification and it showed in order of university hospital, hospital, clinic in facilities and equipment and in order of university hospital, clinic, hospital in distance transportation Analysis of importance of selection factors by sex distinction, it showed that doctors' career were high for both male and female and according to the result of analysis of selection factors by an age, doctors' career variable was measured high and it showed in order of facilities, equipment, distance and convenient transportation. The result of analysis by the form of medical treatment, doctors' career were measured high in all diseases. Facilities and equipment were measured high in case of a corrective operation of eyesight and distance transportation variable showed high in simple eye diseases. According to the result of analysis of habits of searching information by utility frequency, one's own experience in the past(direct visits) was the highest over all and it showed in order of introduction of other ophthalmic department in case of people who go to the institutes many times.