• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.293-308
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• 1997

Sliced inverse regression is a method for reducing the dimension of the explanatory variable X without going through any parametric or nonparametric model fitting process. This method explores the simplicity of the inverse view of regression; that is, instead of regressing the univariate output varable y against the multivariate X, we regress X against y. In this article, we propose bivariate sliced inverse regression, whose method regress the multivariate X against the bivariate output variables $y_1, Y_2$. Bivariate sliced inverse regression estimates the e.d.r. directions of satisfying two generalized regression model simultaneously. For the application of bivariate sliced inverse regression, we decompose the output variable y into two variables, one variable y gained by projecting the output variable y onto the column space of X and the other variable r through projecting the output variable y onto the space orthogonal to the column space of X, respectively and then estimate the e.d.r. directions of the generalized regression model by utilize two variables simultaneously. As a result, bivariate sliced inverse regression of considering the variable y and r simultaneously estimates the e.d.r. directions efficiently and steadily when the regression model is linear, quadratic and nonlinear, respectively.

• The Korean Journal of Applied Statistics
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• v.18 no.2
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• pp.381-393
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• 2005

As a promising technique for dimension reduction in regression analysis, Sliced Inverse Regression (SIR) and an associated chi-square test for dimensionality were introduced by Li (1991). However, Li's test needs assumption of Normality for predictors and found to be heavily dependent on the number of slices. We will provide a unified asymptotic test for determining the dimensionality of the SIR model which is based on the probabilistic principal component analysis and free of normality assumption on predictors. Illustrative results with simulated and real examples will also be provided.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.619-622
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• 2005

적용대상 유역은 낙동강수계로 하였으며 소유역 분할은 총 25개로 하였으며, 강우관측소의 선정과 Thiessen 계수의 산정은 최근에 한국수자원공사에서 새로 추가한 강우관측소를 위주로 대상 연도별로 달리하여 강우관측소를 선정하였다. 강우자료의 결측치는 RDS 방법을 사용하여 보완하였다. 대상연도별 소유역별로 일간 유역 평균 강우량을 산정하였다. 적용 모형의 선정은 한국수자원공사 실무부서에서의 적용사례가 빈번한 SSARR 모형을 최종적으로 선정하였다. SSARR 모형의 입력자료를 물리적 매개변수, 수문기상 매개변수 및 내부처리 매개변수로 구분하여 구축하였고 매개변수의 민감도분석과 함께 모형의 보정을 실시하였다. 민감도 분석 결과, 유역유출과 관련된 매개변수에서는 고수시와 저수시의 경우 지표수와 복류수의 분리하는 매개변수에서 민감도가 크게 나타났다. 저수시의 경우 지하수 중 회귀지하수가 차지하는 비율이 크게 나타났고, 지표수, 복류수, 지하수 및 회귀지하수의 저류시간에서 비교적 큰 민감도를 나타내었다. 1983년부터 2003년까지 21개년에 걸쳐 25개 소유역별로 일평균 자연유출량을 산정하여 이를 이용한 반순, 순, 월 및 연평균 자연유출량을 산정하였다.

• The Journal of the Acoustical Society of Korea
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• v.31 no.3
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• pp.170-178
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• 2012

In the paper, phonetic decision tree of the triphone unit was built for the phoneme-based speech recognition of 640 stations which run by the Korail. The clustering rate was determined by Pearson and Regression analysis to decide threshold used in node splitting. Using the determined the clustering rate, thresholds are automatically decided by the threshold value according to the average clustering rate. In the recognition experiments for verifying the proposed method, the performance improved 1.4~2.3 % absolutely than that of the baseline system.

• Journal of Korea Water Resources Association
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• v.41 no.12
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• pp.1231-1244
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• 2008

The objective of this study is to present a methodology for estimating runoff curve number(CN) using SWAT model which is capable of reflecting watershed heterogeneity such as climate condition, land use, soil type. The proposed CN estimation method is based on the asymptotic CN method and particularly, it uses surface flow data simulated by SWAT. This method has advantages to estimate spatial CN values according to subbasin division and to reflect watershed characteristics because the calibration process has been made by matching the measured and simulated streamflows. Furthermore, the method is not sensitive to rainfall-runoff data since CN estimation is on a daily basis. The SWAT based CN estimation method is applied to Chungju dam watershed. The regression equation of the estimated CN that exponentially decays with the increase of rainfall is presented.