• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.241-254
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• 2017

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.09a
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• pp.60-66
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• 2001

Because of internal voids in insulators give rise to partial discharge(PD), which cause local breakdown and even entire insulation breakdown. Treeing due to PD is one of the main causes of breakdown of the insulating materials and reduction of the insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission(AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation, We analyzed the PD pulse and AE pulses by regression analysis, compared to these obtained the correlation coefficient and determination coefficient by T-distribution and saw that PD and AE pulses show a similar pattern on the whole. This is in agreement with the results of the research by Yoshimura and Fujita.

• Proceedings of the KIEE Conference
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• 1997.07e
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• pp.1761-1763
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• 1997

Because of internal voids in insulators give rise to partial discharge (PD), which cause local breakdown and even entire insulation breakdown. Treeing due to PD is one of the main causes of breakdown of the insulating materials and reduction of the insulation life. Therefore the necessity for establishing a method to diagnose the aging of insulation materials and to predict the breakdown of insulation has become important. From this viewpoint, our studies diagnose insulation degradation using the method of computer sensing system, which has the advantages of PD and acoustic emission (AE) sensing system. To use advantages of these two methods can be used effectively to search for treeing location and PD in some materials. In analysis method of degradation, We analyzed the PD pulse and AE pulses by regression analysis, compared to these obtained the correlation coefficient and determination coefficient by T-distribution and saw that PD and AE pulses show a similar pattern on the whole. This is in agreement with the results of the research by Yoshimura and Fujita.

• Korean journal of food and cookery science
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• v.33 no.3
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• pp.264-274
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• 2017

Purpose: The purpose of this study was to analyze the meal types by cooking methods provided at different meal provision places using the 2015 Korea National Health and Nutrition Examination Survey. Methods: A total of 42,441 meal data on adults from the 2015 Korea National Health and Nutrition Examination Survey were used for analysis. The data were analyzed by complex sample $x^2-test$ of independence and complex sample logistic regression analysis using SPSS 23.0 for Windows. Results: The meal provision place showing the highest frequency was home (60.2%), followed by commercial (32.5%) and institutional foodservices (7.3%). The meal types by cooking method most frequently consumed were rices (18.3%) and kimchis (16.6%). The results of the complex sample logistic regression analysis showed that breads & snacks, steamed or braised dishes, fried dishes, and fresh seasoned vegetables were more likely to be consumed at commercial or institutional foodservices than at home. In addition, noodles & dumplings were more likely to be consumed at commercial places, and Korean soups were consumed at institutional foodservices. Conclusion: From the results of this study, it is suggested to develop recipes for substitution of fried dishes and to develop low sodium recipes at commercial and institutional foodservices. In addition, education of consumers of commercial foodservice is needed to reduce consumption of fried dishes, salted seafoods, and pickled vegetables and encourage consumers to choose meals from institutional foodservice managed by dietitians.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.4
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• pp.505-514
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• 2015

This paper proposes a probabilistic generation assessment model of renewable energy generators(REGs) considering uncertainty of resources, mainly focused on Wind Turbine Generator(WTG) and Solar Cell Generator(SCG) which are dispersed widely in South Korea The proposed numerical analysis method assesses the one day-ahead generation by combining equivalent generation characteristics function and probabilistic distribution function of wind speed(WS) and solar radiation(SR) resources. The equivalent generation functions(EGFs) of the wind and solar farms are established by grouping a lot of the farms appropriately centered on Weather Measurement Station(WMS). First, the EGFs are assessed by using regression analysis method based on typical least square method from the recorded actual generation data and historical resources(WS and SR). Second, the generation of the REGs is assessed by adding the one day-ahead resources forecast, announced by WMS, to the EGFs which are formulated as third order degree polynomials using the regression analysis. Third, a Renewable Energy Generation Assessment System(REGAS) including D/B of recorded actual generation data and historical resources is developed using the model and algorithm predicting one day-ahead power output of renewable energy generators.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.2
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• pp.120-128
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• 2000

At the initial design stage of a new vehicle, the chassis layout has the most important influence on the overall vehicle performance. Most chassis designers have achieved the target performances by trial and error method as well as individual knowhow. Accordingly, a general procedure for determining the optimum location of suspension hard points with respect to the kinematic characteristics needs to be developed. In this paper, a method to optimize the toe angle in the double wishbone type front suspension of the four-wheel-drive vehicle is presented using the design of experiment, multibody dynamic simulation, and optimum design program. The handling performances of two full vehicle models having the initial and optimized toe angle are compared through the single lane change simulation. The sensitive design variables with respect to the kinematic characteristics are selected through the experimental design sensitivity analysis using the perturbation method. An object function is defined in terms of the toe angle among those kinematic characteristics. By the design of experiment and regression analysis, the regression model function of toe angle is obtained. The design variables which make the toe angle optimized ae extracted using the optimum design program DOT. The single lane change simulation and test of the full vehicle model are carried out to survey the handling performances of vehicle with toe angle optimized. The results of the single lane change simulation show that the optimized vehicle has the more improved understeer tendency than the initial vehicle.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.789-792
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• 2004

In various fields as web mining, bioinformatics, statistical data analysis, and so forth, very diversely missing values are found. These values make training data to be sparse. Largely, the missing values are replaced by predicted values using mean and mode. We can used the advanced missing value imputation methods as conditional mean, tree method, and Markov Chain Monte Carlo algorithm. But general imputation models have the property that their predictive accuracy is decreased according to increase the ratio of missing in training data. Moreover the number of available imputations is limited by increasing missing ratio. To settle this problem, we proposed statistical learning theory to preprocess for missing values. Our statistical learning theory is the support vector regression by Vapnik. The proposed method can be applied to sparsely training data. We verified the performance of our model using the data sets from UCI machine learning repository.

• International Journal of Highway Engineering
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• v.14 no.6
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• pp.121-129
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• 2012

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

• Advances in pediatric surgery
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• v.12 no.2
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• pp.202-212
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• 2006

The prognostic factors for extrahepatic biliary atresia (EHBA) after Kasai portoenterostomy include the patient's age at portoenterostomy (age), size of bile duct in theporta hepatis (size), clearance of jaundice after operation (clearance) and the surgeon's experience. The aim of this study is to examine the most significant prognostic factor of EHBA after Kasai portoenterostomy. This retrospective study was done in 51 cases of EHBA that received Kasai portoenterostomy by one pediatric surgeon. For the statistical analysis, Kaplan-Meier method, Logrank test and Cox regression test were used. A p value of less than 0.05 was considered to be significant. Fifteen patients were regarded as dead in this study, including nine cases of liver transplantation. There was no significant difference of survival to age. The age is also not a significant risk factor for survival in this study (Cox Regression test; p = 0.63). There was no significant difference in survival in relation to the size of bile duct. However, bile duct size was a significant risk factor for survival (Cox Regression test; p = 0.002). There was a significant difference in relation to survival and clearance (Kaplan-Meier method; p = 0.02). The clearing was also a significant risk factor for survival (Cox Regression test; p = 0.001). The clearance of jaundice is the most significant prognostic factor of EHBA after Kasai portoenterostomy.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.569-578
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• 2020

By estimating conditional quantile functions of the response, quantile regression (QR) can provide comprehensive information of the relationship between the response and the predictors. In addition, kernel quantile regression (KQR) estimates a nonlinear conditional quantile function in reproducing kernel Hilbert spaces generated by a positive definite kernel function. However, it is infeasible to use the KQR in analysing a massive data due to the limitations of computer primary memory. We propose a divide and conquer based KQR (DC-KQR) method to overcome such a limitation. The proposed DC-KQR divides the entire data into a few subsets, then applies the KQR onto each subsets and derives a final estimator by aggregating all results from subsets. Simulation studies are presented to demonstrate the satisfactory performance of the proposed method.