• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.13-15
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• 2000

In this paper, we deal with the asymptotic properties of the regression quantile estimators in the nonlinear time series regression model. For the sinusodial model which frequently appears fer a time series analysis, we study the strong consistency and asymptotic normality of regression quantile ostinators.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.669-681
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• 2010

Value-at-Risk(VaR) is an important part of risk management in the financial industry. This paper present a VaR forecasting for financial time series based on the quantile regression for GARCH models recently developed by Lee and Noh (2009). The proposed VaR forecasting features the direct conditional quantile estimation for GARCH models that is well connected with the model parameters. Empirical performance is measured by several backtesting procedures, and is reported in comparison with existing methods using sample quantiles.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.49-58
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• 1997

We consider the quantile function for the bootstrapped product limit estimate under left truncation and right censoring model and show its weak convergence. We also obtain bootstrapped confidence bands for the quantile function.

• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.373-384
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• 2003

In this paper, we consider the asymptotic properties of regression quantile estimators for the nonlinear regression model when dependent variables are subject to censoring time, and propose the sufficient conditions which ensure consistency and asymptotic normality for regression quantile estimators in censored nonlinear regression model. Also, we drive the asymptotic relative efficiency of the censored regression model with respect to the ordinary regression model.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.715-725
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• 2014

This study uses a quantile regression analysis to investigate intergenerational economic mobility in Korea. The analysis is based on data from the 1st through 11th waves of the Korean Labor and Income Panel Study (KLIPS) conducted from 1998-2008. The household nature of the data allows us to link parents' incomes to children's incomes at different points in time. Using a quantile regression analysis instead of mean one reveals that the effect of fathers' earnings are different across the conditional distribution of sons' earnings, particularly being larger on the upper quantile than on the lower quantile. After controlling effect of sons' college education by including a dummy variable for the degree, however, the pattern among quantile effects for fathers' earnings is no longer clear. Instead a new pattern emerges that education has a much larger effect on the upper quantiles than on the lower ones. Using nonparametric estimates of conditional density curves based on the quantile regression results, we derive some interesting features in graphical forms, which are not obvious in numerical analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.418-418
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• 2012

현재 전 세계적으로 온실가스 농도 증가로 호우나 가뭄, 대설 등 지역에 따라 서로 상반되는 변화를 가져올 수 있다고 경고되고 있으며, 우리나라에서도 남해안지역과 경기북부지역에서 호우빈도가 증가하는 반면, 충정도 내륙지역과 경상북도에서는 호우빈도가 감소하고 5일 누적 강수량 또한 감소하여, 해당지역에서 가뭄이 발생할 경우 심화될 가능성이 높아진다고 보고된 바 있다. 기후변화 시나리오에 분석결과에서도 우리나라의 경우 평균적으로 강우일수는 작아지며, 강우강도는 커지는 결과들이 도출되었다. 이러한 결과들은 가뭄의 발생가능성이 높아지고 있음을 보여주고 있다. 본 연구에서는 우리나라에서 발생된 가뭄의 특성을 분석하고 가뭄의 특성과 기상인자간의 관계를 Quantile regression 분석을 통해 살펴보고자 한다. 가뭄의 특성과 기상인자(엘니뇨, 강수량 등)의 관계에 있어서 기상인자들의 평균을 이용하는 일반적인 회귀분석은 전체 데이터의 영향에 따른 가뭄특성인자와의 관계를 보여준다. 하지만 강수량과 가뭄과의 관계에서와 같이 강수량의 극값보다는 적은 강수량 혹은 무강우일수가 가뭄과 밀접한 관련을 보여준다. 이러한 점에서 이상치들에 영향을 배재할 수 있는 Quantile regression을 사용하여 Quantile에 따른 기상인자와 가뭄특성과의 관계를 규명하고 평가해 보고자 한다. 본 연구에서 적용한 Quantile Regression 기법은 회귀계수의 추정에 있어서 회귀인자의 신뢰성을 아래와 같은 Quantile-회귀계수 그래프를 통해 분석할 수 있으며, 로버스트 통계량의 특징인 분산이 적은 안정적인 추정량을 확보할 수 있는 장점을 갖는다. 아래식은 Quantile regression의 회귀계수 추정식을 나타낸다. $$arg\;in\;{n\\\;p(y_i-f(x_i,\;z_i,\;{\cdots}))\\ =1}$$ 여기서, $y_i$는 가뭄특성값을 $x_i$, $z_i$, $\cdots$는 기상인자를 나타낸다. $$p(y-q)={{\beta}(y-q)\;y{\geq_-}q \\ (1-{\beta})(q-y)\;y<q}$$ ${\beta}$는 quantile을 나타내며 0< ${\beta}$ <1범위를 갖는다.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.127-145
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• 2009

A novel approach is proposed to effectively estimate the quantile functions of normalized performance indices of reliability constraints in a reliability-based optimization (RBO) problem. These quantile functions are not only estimated as functions of exceedance probabilities but also as functions of the design variables of the target RBO problem. Once these quantile functions are obtained, all reliability constraints in the target RBO problem can be transformed into non-probabilistic ordinary ones, and the RBO problem can be solved as if it is an ordinary optimization problem. Two numerical examples are investigated to verify the proposed novel approach. The results show that the approach may be capable of finding approximate solutions that are close to the actual solution of the target RBO problem.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.285-293
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• 2010

The traditional methods for evaluating response surface designs are alphabetic optimality criteria. These single-number criteria such as D-, A-, G- and V-optimality do not completely reflect the prediction variance characteristics of the design in question. Alternatives to single-numbers summaries include graphical displays of the prediction variance across the design regions. We can suggest the animated quantile plots as the animation of the quantile plots and use these animated quantile plots for comparing and evaluating response surface designs.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.209-215
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• 2009

In regression problem, the SCAD estimator proposed by Fan and Li (2001), has many desirable property such as continuity, sparsity and unbiasedness. In this paper, we extend SCAD penalized regression framework to quantile regression and hence, we propose new SCAD penalized quantile estimator on high-dimensions and also present an efficient algorithm. From the simulation and real data set, the proposed estimator performs better than quantile regression estimator with $L_1$ norm.