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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 219 - 231 (13page)
- DOI
- 10.7465/jkdi.2019.30.1.219
이용수
초록· 키워드
오류제보하기
A linear regression with correlated errors appears in many contexts. A linear regression assumes independent errors in the model but in reality, it is often violated. As a result, correlated errors can severely have an wrong influence on robustness of the linear regression model. Robustness of parameter estimators in the linear regression model can be kept by using M-estimator. It, however, gets robustness at the sake of efficiency whereas minimum Hellinger distance (MHD) estimators attains both robustness and efficiency. In this paper, a newly-presented method is suggested to get the minimum Hellinger distance estimator for the linear regression with correlated errors. It requires an appropriate nonparametric kernel density estimation for correlated data to accomodate a cross-validation estimator. Simulation study and real data study are conducted for the model. In simulation study, the proposed method in the linear regression model with correlated errors presents smaller biases and mean squared errors than M-estimation and the adjusted least squares (ALS) estimation. In real data, the proposed method has smaller standard errors than the other two methods.
목차
Abstract
1. Introduction
2. Minimum Hellinger distance estimation
3. Nonparametric kernel regression estimation with correlated data
4. Other methods
5. Simulation
6. Application to the ice-cream consumption data
7. Concluding remarks
References
참고문헌 (16)
참고문헌 신청
Basu, A. / 1994 / Minimum disparity estimation for continuous models: Efficiency, dis-tributions and robustness / Annals of the Institute of Statistical Mathematics 46:683~705
Beran, R. / 1977 / Minimum Hellinger distance estimates for parametric models / The Annals of Statistics 5:445~463
Brabanter, K. D. / 2011 / Kernel regression in the presence of correlated errors / Journal of Machine Learning Research 12:1955~1976
최국렬 / 2013 / Variable selection in censored kernel regression / 한국데이터정보과학회지 24(1):201~209
Cochrane, D. / 1949 / Application of least squares regression to relationships containing auto-correlated error terms / Journal of the American Statistical Association 44:32~61
Beran, R. / 1977 / Minimum Hellinger distance estimates for parametric models / The Annals of Statistics 5:445~463
Brabanter, K. D. / 2011 / Kernel regression in the presence of correlated errors / Journal of Machine Learning Research 12:1955~1976
최국렬 / 2013 / Variable selection in censored kernel regression / 한국데이터정보과학회지 24(1):201~209
Cochrane, D. / 1949 / Application of least squares regression to relationships containing auto-correlated error terms / Journal of the American Statistical Association 44:32~61
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