Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1146382 | Journal of Multivariate Analysis | 2011 | 19 Pages |
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 nn. An extension would result in bounded local shift sensitivity, also.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Sung Jae Jun, Joris Pinkse, Yuanyuan Wan,