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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국자료분석학회 Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제7권 제3호
- 발행연도
- 2005.1
- 수록면
- 721 - 728 (8page)
이용수
초록· 키워드
오류제보하기
Independent Component Analysis(ICA) is a widely applicable modern method for finding underlying factors or components from multivariate statistical data. In this paper, we consider the application of ICA for multiple regression. When multicollinearity is present in a set of explanatory variables, the ordinary least squares estimate of the individual regression coefficient tend to be unstable and can lead to erroneous inferences. Numerical study shows we can have stable parameter estimates based on ICA regression when multicollinearity exist.
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참고문헌 (10)
참고문헌 신청
BellA J. / 1995a / A non-linear information maximization algorithm that performs blind separation 7, (wski) : 467 ~ 474
BellA J. / 1995b / An information maximization approach to blind separation and blind deconvolution 7, (wski) : 1129 ~ 1159
Chatterjee S / 1977 / Regression analysis by example
Hyvarinen, A / 2001 / Independent component analysis
HyvarinenA / 1999 / The fixed point algorithm and maximum likelihood estimation for independent component analysis 10( (1)) : 1 ~ 5
BellA J. / 1995b / An information maximization approach to blind separation and blind deconvolution 7, (wski) : 1129 ~ 1159
Chatterjee S / 1977 / Regression analysis by example
Hyvarinen, A / 2001 / Independent component analysis
HyvarinenA / 1999 / The fixed point algorithm and maximum likelihood estimation for independent component analysis 10( (1)) : 1 ~ 5
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