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학술저널

회귀분석에서 편의 추정량들의 로버스트화에 대한 연구

A Robust Version of Biased Estimators in Linear Regression

Two problems which often plague researchers using regression techniques are multicollinearity and nonnormal error distributions. A number of authors have proposed robust regression procedure that are robust to nonnormal error distributions and have suggested biased estimation methods for multicollinearity problem. Although we usually think of there two problems separately, in a significant number of practical situations nonnormality and multicollinearity occur simultaneously. Since robust regression estimates are frequently unstable when the design matrix is ill-conditioned, it would be desiable to have a technique for dealing directly with both problems. Askin and Montgomery(1980) discuss augmented robust estimators as a way of combining biased and robust regression techniques. Based on this idea, this paper suggests the robust versions of Nonnegative Garrot and Lasso. It seems necessary that Monte Carlo study be done to compare the performance of the various type of robust biased estimators.

Ⅰ. 서론

Ⅱ. 기존의 추정량

Ⅲ. 편의 추정량의 로버스트 버전(Robust version of biased estimators)

Ⅳ. 결론

참고문헌

Abstract

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