A Combined Robust Estimator Between the Least Squares Estimator and a t-type Regression Estimator

• 발행기관: 한국자료분석학회
• 저자명: Kang-Mo Jung
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.13 No.5, 2235~2242쪽, 전체 8쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2011.10.30

영문 초록

When the distribution of the errors in linear regression follows a normal distribution the least squares estimator is most efficient. However, if it follows a heavy-tailed distribution such as t distribution, then the least squares estimator is no longer efficient. We propose a combined estimator between the least squares estimator and a t-type regression estimator which is efficient even if the errors have a heavy-tailed or thin-tailed distributions. We calculated the asymptotic property of the proposed estimator. The results of simulation showed that the proposed estimator is robust and effective in many situations.

목차

1. Introduction

2. LSE and t-type regression estimator

3. Hybrid Estimator

4. Simulations

5. Discussion

Reference

키워드

asymptotic property heavy-tailed distribution least squares estimator robust t-type regression estimator thin-tailed distribution