• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.197-203
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• 2012

In fitting a regression model, we often encounter data sets which do not follow Gaussian distribution and/or do not have equal variance. In this case estimation of the conditional density of a response variable at a given design point is hardly solved by a standard least squares method. To solve this problem, we propose a simple method to estimate the distribution of the fitted vales under heteroscedasticity using the idea of quantile regression and the histogram techniques. Application of this method to a real data sets is given.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.815-826
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• 2012

The underestimating trend using existing ordinary regression (OR) based trend analysis has been a well-known problem. The existing OR method based on least squares approximate the conditional mean of the response variable given certain values of the time t, and the usual assumption of the OR method is normality, that is the distribution of data are not dissimilar form a normal distribution. In this regard, this study proposed a quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. This study assess trend in annual daily maximum rainfall series over 64 weather stations through both in OR and QR approach. The QR method indicates that 47 stations out of 67 weather stations are a strong upward trend at 5% significance level while OR method identifies a significant trend only at 13 stations. This is mainly because the OR method is estimating the condition mean of the response variable. Unlike the OR method, the QR method allows us flexibly to detect the trends since the OR is designed to estimate conditional quantiles of the response variable. The proposed QR method can be effectively applied to estimate hydrologic trend for either non-normal data or skewed data.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.8
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• pp.161-173
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• 2020

This study examines the nonlinear relationship between financial development and economic growth in Pakistan using the threshold regression model for the period 1980-2017. We also employed quantile regression with 0.25, 0.50, and 0.75 quantiles of conditional distribution. The quantile regression is based on minimizing of sum of squared residuals. The result indicates that economic growth responds positively to financial development when the level of financial development surpasses the threshold value of 0.151. However, when financial development lies below the threshold value (that is, 0.151), its impact on economic growth is negative. Thus, when financial development of Pakistan surpasses the threshold level, it contributes more towards economic growth since greater level of financial development contributes more to boosts economic growth. This finding reveals that economic growth reacts differently to financial development, and the relationship between financial development and economic growth is U-shaped in Pakistan. Among the other variables, physical capital, labor force, and government expenditure exert a positive effect on economic growth. Furthermore, inflation rate and trade openness have an insignificant impact on economic growth. The results of quantile regression also confirm the non-linear relationship between financial development and economic growth in Pakistan. The finding of this study suggests revamping of financial sector policies in Pakistan.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.211-222
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• 2013

We consider the problem of nonparametrically estimating the conditional quantile function from censored data and propose new estimators here. They are based on local logistic regression technique of Lee et al. (2006) and "double-kernel" technique of Yu and Jones (1998) respectively, which are modified versions under random censoring. We compare those with two existing estimators based on a local linear fits using the check function approach. The comparison is done by a simulation study.

• 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 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 Korean Academic Society of Home Health Care Nursing
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• v.18 no.2
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• pp.81-87
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• 2011

Purpose: The aim of this study was to estimate optimization model of stay time in EMC. Methods: Data were collected at an EMC in a hospital using medical records from June to August in 2007. The sample size was 8,378. The data were structured by stay time for doctor visit, decision making, and discharge from EMC. Descriptive statistics were used to find out general characteristics of patients. Average mean and quantile regression models were adopted to estimate optimized stay time in EMC. Results: The stay times in EMC were highly skewed and non-normal distributions. Therefore, average mean as an indicator of optimal stay time was not appropriate. The total stay time using conditional quantile regression model was estimated about 110 min, that was about 166 min shorter than estimated time using average mean. Conclusion: According to these results, we recommend to use a conditional quantile regression model to estimate optimal stay time in EMC. We suggest that this results will be used to develop a guideline to manage stay time more effectively in EMC.

• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.3-17
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• 2015

In Korea, the private education spending of the households accounted for about 3% of GDP and such a education fever has been associated with the financial burden of households. The main purpose of this paper is to investigate the effects of regional education environment on the private education expenditure of the households using the Korean Labor and Income Panel Survey(KLIPS) data. The quantile regression model is used to examine whether the effects of regional education environment such as the degree of education fever differ across the 'quantiles' in the conditional distribution of private education expenditure. The empirical results showed that the amount of private education expenditure is under the influence of the regions where the households reside. In addition, it was found that the private education spending of the households in the upper quantile groups are more likely to be affected by the regional education environments than those in the lower quantile groups.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1531-1541
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• 2013

This paper proposes a new method of VaR forecasting using the conditional autoregressive VaR (CAViaR) models and information criteria. Instead of using a single CAViaR model, we propose to utilize several candidate CAViaR models during a forecasting period. By adopting the Akaike and Bayesian information criteria for quantile regression, we can update not only parameter estimates but also the CAViaR specifications. We also propose extended CAViaR models with a constant location parameter. An empirical study is provided to examine the performance of the proposed method. The results suggest that our method shows more stable performance than those using a single specification.