Abstract
We propose a new robust estimator of the regression coefficients in a linear regression model. The proposed estimator is the only robust estimator based on integration rather than optimization. It allows for dependence between errors and regressors, is n-consistent, and asymptotically normal. Moreover, it has the best achievable breakdown point of regression invariant estimators, has bounded gross error sensitivity, is both affine invariant and regression invariant, and the number of operations required for its computation is linear in n. An extension would result in bounded local shift sensitivity, also.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 828-846 |
| Number of pages | 19 |
| Journal | Journal of Multivariate Analysis |
| Volume | 102 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 1 2011 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Numerical Analysis
- Statistics, Probability and Uncertainty
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N-Consistent robust integration-based estimation. / Jun, Sung Jae; Pinkse, Joris; Wan, Yuanyuan.
In: Journal of Multivariate Analysis, Vol. 102, No. 4, 01.04.2011, p. 828-846.Research output: Contribution to journal › Article
TY - JOUR
T1 - N-Consistent robust integration-based estimation
AU - Jun, Sung Jae
AU - Pinkse, Joris
AU - Wan, Yuanyuan
PY - 2011/4/1
Y1 - 2011/4/1
N2 - We propose a new robust estimator of the regression coefficients in a linear regression model. The proposed estimator is the only robust estimator based on integration rather than optimization. It allows for dependence between errors and regressors, is n-consistent, and asymptotically normal. Moreover, it has the best achievable breakdown point of regression invariant estimators, has bounded gross error sensitivity, is both affine invariant and regression invariant, and the number of operations required for its computation is linear in n. An extension would result in bounded local shift sensitivity, also.
AB - We propose a new robust estimator of the regression coefficients in a linear regression model. The proposed estimator is the only robust estimator based on integration rather than optimization. It allows for dependence between errors and regressors, is n-consistent, and asymptotically normal. Moreover, it has the best achievable breakdown point of regression invariant estimators, has bounded gross error sensitivity, is both affine invariant and regression invariant, and the number of operations required for its computation is linear in n. An extension would result in bounded local shift sensitivity, also.
UR - http://www.scopus.com/inward/record.url?scp=79953869315&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79953869315&partnerID=8YFLogxK
U2 - 10.1016/j.jmva.2011.01.003
DO - 10.1016/j.jmva.2011.01.003
M3 - Article
VL - 102
SP - 828
EP - 846
JO - Journal of Multivariate Analysis
JF - Journal of Multivariate Analysis
SN - 0047-259X
IS - 4
ER -