• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.2
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• pp.63-75
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• 2018

This study analyzed the long-term linear trends of the sea surface height around the Korea marginal seas for the period of 1993~2016 by using quantile regression. We found significant difference about 2~3 mm/year for the linear trend between OLS (ordinary least square) and median (50%) quantile regression especially in the Yellow Sea, which is affected by extreme events. Each area shows different trend for each quantile (lower (1%), median (50%) and upper (99%)). Most areas of the Yellow Sea show increasing trend in both low and upper quantile, but significant "upward divergence tendency". This implies that significant increasing trend of upper quantile is higher than that of lower quantile in this area. Meanwhile, South Sea of Korea generally shows "upward convergence tendency" representing that increasing trend of upper quantile is lower than that of lower quantile. This study also confirmed that these tendencies can be eliminated by removing major tidal components from the harmonic analysis. Therefore, it is assumed that the regional characteristics are related to the long term change of tide amplitude.

• Journal of Preventive Medicine and Public Health
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• v.54 no.2
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• pp.145-152
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• 2021

Objectives: This study aimed to identify regional differences in the high-risk drinking rate among yearly alcohol users in Korea and to identify relevant regional factors for each quintile using quantile regression. Methods: Data from 227 counties surveyed by the 2017 Korean Community Health Survey (KCHS) were analyzed. The analysis dataset included secondary data extracted from the Korean Statistical Information Service and data from the KCHS. To identify regional factors related to the high-risk drinking rate among yearly alcohol users, quantile regression was conducted by dividing the data into 10%, 30%, 50%, 70%, and 90% quantiles, and multiple linear regression was also performed. Results: The current smoking rate, perceived stress rate, crude divorce rate, and financial independence rate, as well as one's social network, were related to the high-risk drinking rate among yearly alcohol users. The quantile regression revealed that the perceived stress rate was related to all quantiles except for the 90% quantile, and the financial independence rate was related to the 50% to 90% quantiles. The crude divorce rate was related to the high-risk drinking rate among yearly alcohol users in all quantiles. Conclusions: The findings of this study suggest that local health programs for high-risk drinking are needed in areas with high local stress and high crude divorce rates.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.489-499
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• 2018

Jeonse is a unique property rental system in Korea in which a tenant pays a part of the price of a leased property as a fixed amount security deposit and gets back the entire deposit when the tenant moves out at the end of the tenancy. Jeonse deposit is very important in the Korean real estate market since it is directly related to the residential property sales price and it is a key indicator to predict future real estate market trend. Jeonse deposit data shows a skewed and heteroscedastic distribution and the commonly used mean regression model may be inappropriate for the analysis of Jeonse deposit data. In this paper, we apply a Bayesian quantile regression model to analyze Jeonse deposit data, which is non-parametric and does not require any distributional assumptions. Analysis results show that the quantile regression coefficients of most explanatory variables change dramatically for different quantiles. The regression coefficients of some variables have different signs for different quantiles, implying that even the same variable may affect the Jeonse deposit in the opposite direction depending on the amount of deposit.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.125-132
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• 2015

It is current situation that a number of issues are being raised how the weight is calculated from customer satisfaction survey. This study investigated how the weight of satisfaction for each quantile is different by comparing ordinary least square regression model to quantile regression model and carried out bootstrap verification to find the influence difference of regression coefficient for each quantile. As the analysis result of using R(Quantreg package) that is open software, it appeared that there was the influence size of satisfaction factor along study result and quantile and there was the significant difference statistically regarding regression coefficient for each quantile. So, to use quantile regression model that offers the influence of satisfaction factor for each customer group along satisfaction level would contribute to plan the quantitative convergence policy for customer satisfaction.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.404-404
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• 2022

효율적인 수자원 관리를 위해 빈도해석을 통한 수문 자료의 통계적 특성을 고려하여 정확한 확률강수량을 산정해야 한다. 지점빈도해석은 지점 자료만을 이용하여 확률강수량을 산정하기 때문에 정확도를 높이기 위해서는 자료 확충이 필요하지만, 지점별로 활용할 수 있는 자료가 제한적이며 지점마다 변동성이 크다. 지역빈도해석은 수문기상학적으로 동질한 주변 지점들의 자료를 모두 포함해서 빈도해석을 수행함으로써 지역에 대한 통합 결과를 제시하고 자료에 대한 신뢰성 확보가 가능하다. 일반적으로 빈도해석은 자료에 적합한 확률분포 기반으로 수행되지만 확률분포 선정과정에 따라 결과는 상이하다. 본 연구에서는 지역빈도해석에서 확률강수량 산정방법으로 Quantile Regression(QR)을 적용하였다. QR 기반의 빈도해석은 확률분포 아니라 자료 자체로 확률강수량을 산정하여 기존의 확률분포 기반의 빈도해석에서 발생했던 불확실성을 개선하였다. 또는, 확률강수량의 시간에 따른 변동성도 고려되어 바정상성 빈도해석도 가능하다. 최종적으로 본 연구에서 소개된 지역빈도해석 결과와 기존의 지역빈도해석 결과 비교 검증하였다.

• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.153-164
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• 2022

In this study, we estimate the quantile-regression framework of the shipping industry for the Capesize used ship, which is a typical raw material transportation from January 2000 to December 2021. This research aims two main contributions. First, we analyze the relationship between the Capesize used ship, which is a typical type in the raw material transportation market, and the freight market, for which mixed empirical analysis results are presented. Second, we present an empirical analysis model that considers the structural transformation proposed in the Hyunsok Kim and Myung-hee Chang(2020a) study in quantile-regression. In structural change investigations, the empirical results confirm that the quantile model is able to overcome the problems caused by non-stationarity in time series analysis. Then, the long-run relationship of the co-integration framework divided into long and short-run effects of exogenous variables, and this is extended to a prediction model subdivided by quantile. The results are the basis for extending the analysis based on the shipping theory to artificial intelligence and machine learning approaches.

• Journal of dental hygiene science
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• v.19 no.4
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• pp.271-278
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• 2019

Background: This study aimed to examine the factors that influence clinical performance of dental hygiene students to provide useful data for developing strategies to improve clinical competence. Methods: The effects of variables on clinical competence by quantile level were analyzed using quantile regression analysis in 247 dental hygiene students. Quantile regression and multiple regression analyses were conducted using the Stata 11.0 program to analyze predictors of clinical competence. Results: The clinical competence score of dental hygiene students was 42.69±5.90, the satisfaction of clinical practice was 49.90±7.44, the clinical practice stress was 50.62±7.37, and the professional self-concept was 31.68±4.41. Empathy was the highest at 50.87±4.93. Multiple regression analysis showed that school year, stress from clinical training, satisfaction with clinical training, professional self-concept, and empathy had significant impact on clinical competence. Quantile regression analysis showed that the effects varied depending on the clinical competence level. School year and professional self-concept had a significant positive effect, regardless of the clinical competence level, while empathy had a significant positive effect at the top 10% (Q90) of the clinical competence level. Satisfaction with clinical practice affected clinical competence at Q25, Q50, and Q90. Stress from clinical practice had significant effects at Q25, Q50, and Q90 (p<0.05). Conclusion: According to the study results, different factors affected clinical competence according to the quantile of clinical competence. This study provides valuable implications for designing clinical competence enhancement programs and strategies. In addition, objective indicators for considering factors that may affect the clinical competence, such as academic competence and satisfaction of practice hospitals, are expected to require detailed analysis and measures.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.3
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• pp.118-128
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• 2016

As the rural areas have experienced the population reduction and the aging, the service level of public transit decreases. This study analyzes the effecting factor to user satisfaction of demand response transport(DRT) as alternative to rural public transit by the quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. Jeonbuk Province tested DRT operations in Dongsang of Wanju County and Sannae of Jeongup City each in 2015. The user DRT satisfaction of Wanju was higher than one of Jeongup in basic statistics analysis. The difference in satisfaction between higher quantile and lower quntile of Wanju is smaller than one of Jeongupy as a result of quantile regression analysis. Also, Wanju DRT continues the second test operation of DRT as satisfaction from Ordinary Least Squares(OLS) close to higher satisfaction quantile.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.533-545
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• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• 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.