• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.773-786
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• 2016

Multiple quantile regression that simultaneously estimate several conditional quantiles of response given covariates can provide a comprehensive information about the relationship between the response and covariates. Some quantile estimates can cross if conditional quantiles are separately estimated; however, this violates the definition of the quantile. To tackle this issue, multiple quantile regression with non-crossing constraints have been developed. In this paper, we carry out a comparison study on several popular methods for non-crossing multiple linear quantile regression to provide practical guidance on its application.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.753-762
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• 2020

With an increasing interest in integrating artificial intelligence (AI) into interview processes, the Republic of Korea (ROK) army is trying to lead and analyze AI-powered interview platform. This study is to analyze the AI interview data using a unified non-crossing multiple quantile tree (UNQRT) model. Compared to the UNQRT, the existing models, such as quantile regression and quantile regression tree model (QRT), are inadequate for the analysis of AI interview data. Specially, the linearity assumption of the quantile regression is overly strong for the aforementioned application. While the QRT model seems to be applicable by relaxing the linearity assumption, it suffers from crossing problems among estimated quantile functions and leads to an uninterpretable model. The UNQRT circumvents the crossing problem of quantile functions by simultaneously estimating multiple quantile functions with a non-crossing constraint and is robust from extreme quantiles. Furthermore, the single tree construction from the UNQRT leads to an interpretable model compared to the QRT model. In this study, by using the UNQRT, we explored the relationship between the results of the Army AI interview system and the existing personnel data to derive meaningful results.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.915-922
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• 2013

Quantile regression can estimate multiple conditional quantile functions of the response, and as a result, it provide comprehensive information of the relationship between the response and the predictors. However, when estimating several conditional quantile functions separately, two or more estimated quantile functions may cross or overlap and consequently violate the basic properties of quantiles. In this paper, we propose a new stepwise method to estimate multiple non-crossing quantile functions using constraints on the kernel coefficients. A simulation study are presented to demonstrate satisfactory performance of the proposed method.

• Management & Information Systems Review
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• v.32 no.1
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• pp.173-194
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• 2013

The purpose of this study drew the core elements of ISO26000 by analyzing the elements suitable to the characteristics of casino companies, and examined the influence of the core elements of ISO26000 on organizational trust following the level of organizational trust of employees. As a result of the factor analysis, among the 7 measurement items of ISO26000, improvement of governance and fair operating practices were simplified into one factor and thus 6 factors were used for empirical analysis. Therefore, multiple regression analysis using least square method was conducted to examine the impacts of the 6 elements. As a result, 5 variables excluding human rights had significant impacts on the organizational trust. Concretely, the 5 core elements of ISO26000 (labor practices, governance and fair operation, consumer issues, environment and community social and economic development) had significant impact on organization trust in order. In addition, the results of quantile regression analysis show the core elements of ISO26000 had different impacts on organizational trust depending on the level of organizational trust of employees.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1457-1469
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• 2017

Private education expenses is one of the key issues in Korea and there have been many discussions about it. Academically, most of previous researches for private education expenses have used multiple regression linear model based on ordinary least squares (OLS) method. However, if the data do not satisfy the basic assumptions of the OLS method such as the normality and homoscedasticity, there is a problem with the reliability of estimations of parameters. In this case, quantile regression model is preferred to OLS model since it does not depend on the assumptions of nonnormality and heteroscedasticity for the data. In the present study, the data from a survey on private education expenses, conducted by Statistics Korea in 2015 has been analyzed for investigation of the impacting factors for private education expenses. Since the data do not satisfy the OLS assumptions, quantile regression model has been employed in Bayesian approach by using gibbs sampling method. The analysis results show that the gender of the student, parent's age, and the time and cost of participating after school are not significant. Household income is positively significant in proportion to the same size for all levels (quantiles) of private education expenses. Spending on private education in Seoul is higher than other regions and the regional difference grows as private education expenditure increases. Total time for private education and student's achievement have positive effect on the lower quantiles than the higher quantiles. Education level of father is positively significant for midium-high quantiles only, but education level of mother is for all but low quantiles. Participating after school is positively significant for the lower quantiles but EBS textbook cost is positively significant for the higher quantiles.

• Journal of Nutrition and Health
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• v.49 no.5
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• pp.335-346
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• 2016

Purpose: This study was conducted to conduct dietary assessment of Korean adults according to intake of Korean soup and stew. Methods: To accomplish this study, 20,926 adults aged 19 yr or higher who participated in the dietary intake survey (24 h recall method) were analyzed from the data of the 2011~2014 Korea National Health and Nutrition Examination Survey (KNHANES). The items included in the soup and stew were guk, tang, jjigae, jijimi, and jeongol. Results: Intakes of soup and stew of subjects were divided into quartiles; intake range by quartile was Q1: < 22.96 g, Q2: 22.96~98.75 g, Q3: 98.75~212.23 g, and Q4: $${\geq_-}212.24g$$. In the case of the Q4 group, male, married, employed, higher educated, and high income subjects showed increased intakes of soup and stew. In addition, sodium intake among nutrient intakes increased from 3,849.04 mg in Q1 group to 5,363.57 mg in Q4 group. Intakes of cereals/grains, potatoes/starches, legumes, seeds/nuts, vegetables, mushrooms, fruits, meat, fishes/shell fishes, milks/dairy products, oils/fats, and seasonings among all foods significantly increased from Q1 group to Q4 group. Lastly, in the multivariable regression analysis, male, higher age, married, eating breakfast, consumption of snacks, prepared meals from home/institution, average eating-out frequency per week, energy consumption, and sodium intake related parameters increased intake of Korean soup and stew. Conclusion: Excessive intake of sodium was related to high blood pressure and metabolic syndrome. Thus, there are needs to improve dietary guidelines and nutrition education for balanced intake of soup and stew.