Robust Statistical Methods in Variable Selection

• 발행기관: 한국자료분석학회
• 저자명: 정강모(Kang-Mo Jung)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.10 No.6, 3057~3065쪽, 전체 9쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2008.12.30

영문 초록

Variable selection is an important research field in linear regression modeling with high-dimensional predictors. We proposed a robust penalized regression estimator which provides automatically selection of variables and estimation of regression parameters together. It is based on the least absolute deviation and the non-convex penalty function, the smoothly clipped absolute deviation suggested by Fan and Li(2001). We developed the algorithm for the proposed estimator using the local quadratic approximation and chose the tuning parameter of the penalty function. The algorithm needs only linear equations and so we can obtain quickly the estimators. The simulation result shows that the proposed estimator is robust and efficient for non-normal cases.

목차

1. Introduction

2. Methods

3. Algorithm

4. Simulation

5. Concluding Remarks

References

키워드

least absolute deviation local quadratic approximation robust methods smoothly clipped absolute deviation variable selection