主成分回歸와 制限回歸에 관한 小考

• 영문명: A Note on Principal Component Regression and Restricted Regression
• 발행기관: 건국대학교 경제경영연구소
• 저자명: Ahn Byoung Jin(安秉珍)
• 간행물 정보: 『상경연구』제10권, 227~235쪽, 전체 9쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 1985.08.10

영문 초록

Collinearity among independent variables in multiple linear regression can have sever effects on the precision of prediction equation. To overcome this problem, many alternate type of biased estimation have been proposed. In this we have compared the ordinary least squares, principal component and restricted estimators of regression coefficient using mean-squared error sum criterion. we have shown that the principal component estimator is identical to the least squared estimator with a special restriction and using this fact we have proposed a specific method which determines the number of latent vectors to be deleted in principal component regression. Since the proposed method in this paper contains the least squares estimator which may be poor under collinearity, the estimation problem of proposed criterion deserved further consideration.

목차

Ⅰ. 머리말

Ⅱ. 판단기준(Criterion)

Ⅲ. 판단기준의 추정

Ⅳ. 主成分回歸와 制限回歸와의 관계

Ⅴ. 맺음말

參考文獻

SUMMARY