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Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood
| Content Provider | Scilit |
|---|---|
| Author | Bondell, Howard D. Stefanski, Leonard A. |
| Copyright Year | 2013 |
| Description | Large- and finite-sample efficiency and resistance to outliers are the key goals of robust statistics. Although often not simultaneously attainable, we develop and study a linear regression estimator that comes close. Efficiency obtains from the estimator's close connection to generalized empirical likelihood, and its favorable robustness properties are obtained by constraining the associated sum of (weighted) squared residuals. We prove maximum attainable finite-sample replacement breakdown point, and full asymptotic efficiency for normal errors. Simulation evidence shows that compared to existing robust regression estimators, the new estimator has relatively high efficiency for small sample sizes, and comparable outlier resistance. The estimator is further illustrated and compared to existing methods via application to a real data set with purported outliers. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3747015/pdf |
| Ending Page | 655 |
| Page Count | 12 |
| Starting Page | 644 |
| ISSN | 01621459 |
| e-ISSN | 1537274X |
| DOI | 10.1080/01621459.2013.779847 |
| Journal | Journal of the American Statistical Association |
| Issue Number | 502 |
| Volume Number | 108 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2013-06-01 |
| Access Restriction | Open |
| Subject Keyword | Mathematical Social Sciences Statistics and Probability Asymptotic Efficiency Breakdown Point Constrained Optimization Efficient Estimation Empirical Likelihood Exponential Tilting Least Trimmed Squares Robust Regression Weighted Least Squares |
| Content Type | Text |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |
