A Robust Hybrid Estimator for Linear Regression

• 발행기관: 한국자료분석학회
• 저자명: Kang-Mo Jung
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.12 No.6, 2981~2990쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2010.12.30

영문 초록

The least squares estimator in linear regression is most efficient when the errors are generated from thin tailed distributions, while the least absolute deviation estimator is most efficient when the errors are generated from thick- tailed distributions. A combination of the least squares estimator and the least absolute deviation estimator provide better results whether the errors come from thin or thick distributions. Even though the least absolute deviation estimator is not sensitive to regression outliers, it is not robust to leverage points. We propose a robust hybrid estimator based on a linear combination of the least squares estimator and a weighted least absolute deviation estimator. The results of simulation and a numerical example showed that the proposed estimator is very effective in many situations and is robust.

목차

1. Introduction

2. LSE, LAD and Weighted LAD

3. Hybrid Estimator

4. Simulations

5. Real Data Example

References

키워드

Hybrid estimator least absolute deviation estimator least squares estimator leverage points regression outliers robust weighted least absolute deviation