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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2004.6
- 수록면
- 307 - 316 (10page)
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초록· 키워드
오류제보하기
Principal Component Regression(PCR) and Partial Least Squares Regression(PLSR) are the two most popular regression techniques in chemometsics. In the field of chemometrics usually the number of regressor variables greatly exceeds the number of observation. So we have to reduce the number of regressors to avoid the identifiability problem. In this paper we compare PCR and PLSR techniques combined with various robust regression methods including regression depth estimation. We compare the efficiency, goodness-of-fit and robustness of each estimators under several contamination schemes.
목차
등록된 정보가 없습니다.
참고문헌 (12)
참고문헌 신청
[학술지(정기간행물)] - / 1993 / an Alternative Approach to Partial Least Squares Regression : 251 ~ 263
[학술지(정기간행물)] - / 2003 / Robust PCR and Robust PLSR: a Comparative Study" to appear in
[학술지(정기간행물)] - / 1993 / A Statistical View of Some Chemometrics Regression Tools tech (metrics) : 109 ~ 148
[학술지(정기간행물)] - / 2002 / ROBPCA : a New Approach to Robust Principal Component Analysis
[학술지(정기간행물)] - / 2003 / Robust Methods for Partial Least Squares Regression 17 : 537 ~ 549
[학술지(정기간행물)] - / 2003 / Robust PCR and Robust PLSR: a Comparative Study" to appear in
[학술지(정기간행물)] - / 1993 / A Statistical View of Some Chemometrics Regression Tools tech (metrics) : 109 ~ 148
[학술지(정기간행물)] - / 2002 / ROBPCA : a New Approach to Robust Principal Component Analysis
[학술지(정기간행물)] - / 2003 / Robust Methods for Partial Least Squares Regression 17 : 537 ~ 549
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UCI(KEPA) : I410-ECN-0101-2018-041-001392065