• Communications for Statistical Applications and Methods
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• v.26 no.1
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• pp.35-46
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• 2019

Multivariate Confidence Region (MCR) cannot be used to obtain the confidence region of the mean vector of multivariate data when the normality assumption is not satisfied; however, the Quantile Confidence Region (QCR) could be used with a Multivariate Quantile Vector in these cases. The coverage rate of the QCR is better than MCR; however, it has a disadvantage because the QCR has a wide shape when the probability density function follows a bimodal form. In this study, we propose a Quantile Confidence Region using the Highest density (QCRHD) method with the Highest Density Region (HDR). The coverage rate of QCRHD was superior to MCR, but is found to be similar to QCR. The QCRHD is constructed as one region similar to QCR when the distance of the mean vector is close. When the distance of the mean vector is far, the QCR has one wide region, but the QCRHD has two smaller regions. Based on these features, it is found that the QCRHD can overcome the disadvantages of the QCR, which may have a wide shape.

• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.185-195
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• 2022

Purpose - This study attempts to analyze the determinants of inventory turnover by applying quantile regression analysis. Design/methodology/approach - By selecting the gross margin, capital intensity, and sale surprise as the determinants of inventory turnover, we investigate their effects on inventory turnover at the several quartiles (10%, 25%, 50%, 75%, 90%) of inventory turnover with quantile regression analysis. Findings - The effects of gross margin and capital intensity on inventory turnover are different for each quartile. But the effects of sale surprise on inventory turnover are not different for each quartile. Research implications or Originality -This study is the first attempt to examine the effects of inventory turnover determinants on inventory turnover by applying quantile regression analysis was not employed in the prior studies. Thus, this study is meaningful in that it shows the possible way to review inventory management strategies that can be applied differently to the firms with different inventory turnover levels.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.319-331
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• 2022

In this paper, we develop a new statistical model to forecast the PM_2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM_2.5 level well.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1361-1371
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• 2016

Quantile regression provides a variety of useful statistical information to examine how covariates influence the conditional quantile functions of a response variable. However, traditional quantile regression (which assume a linear model) is not appropriate when the relationship between the response and the covariates is a nonlinear. It is also necessary to conduct variable selection for high dimensional data or strongly correlated covariates. In this paper, we propose a penalized quantile regression tree model. The split rule of the proposed method is based on residual analysis, which has a negligible bias to select a split variable and reasonable computational cost. A simulation study and real data analysis are presented to demonstrate the satisfactory performance and usefulness of the proposed method.

• 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.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1095-1106
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• 2016

The quantile regression method proposed by Koenker et al. (1978) focuses on conditional quantiles given by independent variables, and analyzes the relationship between response variable and independent variables at the given quantile. Considering the linear programming used for the estimation of quantile regression coefficients, the model fitting job might be difficult when large data are introduced for analysis. Therefore, dimension reduction (or variable selection) could be a good solution for the quantile regression of large data sets. Regression tree methods are applied to a variable selection for quantile regression in this paper. Real data of Korea Baseball Organization (KBO) players are analyzed following the variable selection approach based on the regression tree. Analysis result shows that a few important variables are selected, which are also meaningful for the given quantiles of salary data of the baseball players.

• Korean Journal of Adult Nursing
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• v.27 no.3
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• pp.273-282
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• 2015

Purpose: The objective of this study was to identify the predictors of self-care behaviors among elderly patients with hypertension using quantile regression method. Methods: A total of 253 elderly patients diagnosed with hypertension was recruited via 3 different medical clinics for the study. The quantile regression and a liner regression was conducted using Stata 12.0 program by analyzing predictors of self-care behaviors. Results: In the ordinary least square, self-efficacy, period of disease, and education level explained 42% of the variance in self-care activities. In the quantile regression, affecting predictors of self-care behaviors were self-efficacy for all quantiles, the period of disease for from 60% quantile to 90% quantile, education level for 20%, 30%, and 50% quantiles, economic status for 10%, 50%, and 60% quantiles, age for 10%, 70% quantiles, fatigue for 10% quantile, knowledge about hypertension for 10% and 20% quantiles, and depression for 30% and 40% quantiles. Conclusion: The affecting predictors of self-care behaviors among elderly with hypertension were different from the level of self-care behaviors. These results indicated the significance in assessing predictors according to the level of self-care behaviors when clinical nurses examine the patients' health behaviors and plan any intervention strategies. Specially, education level and knowledge about hypertension were the significant predictors of self-care activities for low quantiles. Clinical nurses may promote self-care activities of the given population though health education programs.

• Health Policy and Management
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• v.25 no.3
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• pp.240-251
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• 2015

Background: Dentist's income is quite variable. We investigate the factors underlying the distribution of dental revenue and dentist income. Methods: Financial and structural variables of private dental practices(N=13,967) were examined with 2010 Economic Census microdata which include non-insurance revenue. We conducted quantile regression method(QRM) and ordinary least square(OLS) in treating skewness and heteroskedasticity of distributions. The effective estimation for the upper and lower range of distribution becomes possible by QRM. Results: Mid-career dentists are shown to have higher revenue and income. Male dentists achieve the higher revenue and income than female dentists in all quantiles. Group practices show lower income per owner than solo practices significantly. The revenue and income are increased with increasing size of clinics. The high cost in renting the clinic office is found to have a big positive effect on the revenue but a little positive effect on the income. Interestingly the density of dentists shows negative effect on the lowest quantile of the revenue but positive effect on the highest quantile. The lowest quantile of the revenue in the capital areas have the relatively high revenue. The lowest quantile of the income in metropolitan city show higher income than those in other areas significantly. Conclusion: The suggested QRM is shown to have more effective and efficient tool in finding out determinants of dentists' revenue and income of our concern. The results of this study are expected to be employed for dentists preparing for the opening practices in their organizational settings and locational selections. The distributional efficiency of dental human resources could be accomplished if policy makers guide dentists with this knowledge.

• 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.

• The Journal of the Korea Contents Association
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• v.16 no.4
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• pp.426-433
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• 2016

This study investigates the effects of entrepreneurship and corporate social responsibility (CSR) on firm performance. I use the conditional quantile regression as well as the ordinary least square (OLS) with 300 samples, only medium and small size companies. I found firstly, entrepreneurship affected overall positively firm performance in the all quantile levels. Secondly, CSR also have a positive impact on firm performance in the overall all quantile levels. By these results, I recommend that entrepreneurship and CSR should a positive impact on the firm performance for the small and medium business companies.