|Title||Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Bondell, Howard D., and Leonard A. Stefanski|
|Journal||J Am Stat Assoc|
|Date Published||2013 Jan 01|
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.
|Alternate Journal||J Am Stat Assoc|
|Original Publication||Efficient robust regression via two-stage generalized empirical likelihood.|
|PubMed Central ID||PMC3747015|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA085848 / CA / NCI NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood.