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• W2035375555 abstract "기존 Ordinary Regression (OR) 방법을 이용한 경향성 분석은 경향성을 과소평가하는 문제점을 나타낸다. 이러한 점에서 본 연구에서는 자료의 정규분포 가정과 평균을 중심으로 경향성 평가가 이루어지는 기존 Ordinary Regression (OR) 방법을 개선한 Quantile Regression (QR) 방법을 제안하였다. 본 연구에서는 64개 강우 관측지점의 연 최대 극대강수량 자료에 대하여 QR 방법과 OR 방법에 대하여 통계적 성능을 평가하였다. QR 방법의경향성 분석결과 47개 지점에서 5% 오차수준 내에서 t-검정을 통과한 반면 OR 방법에서는 13개 지점 만이 통계적 유의성을 가지는 것으로 나타났다. 이는 OR 방법이 자료의 평균을 중심으로 경향성을 평가하는 기법인데 반해 QR은 자료의 다양한 분위에서 경향성을 평가함으로써 극대 및 극소 부분에서의 경향성을 보다 유연하게 감지하는 이유로 판단된다. QR 방법을 통한 경향성 평가는 평균 중심의 해석문제점을 개선할 수 있으며 자료가 정규분포를 따르지 않거나 왜곡된 분포형태를 갖는 자료의 수문학적 경향성 평가에 유용하게 사용될 수 있을 것으로 판단된다. 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." @default.
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• W2035375555 date "2012-08-31" @default.
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• W2035375555 title "Trend Analysis of Extreme Precipitation Using Quantile Regression" @default.
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• W2035375555 doi "https://doi.org/10.3741/jkwra.2012.45.8.815" @default.
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