Outlier Robust Model Selection in Linear Regression
We propose a new approach to the selection of regression models based on combining a robust penalized criterion and a robust conditional expected prediction loss function that is estimated using a stratified bootstrap. Both components of the procedure use robust criteria (i.e., robust p-functions) rather than squared error loss to reduce the effects of large residuals and poor bootstrap samples. A key idea is to separate estimation from model selection by choosing estimators separately from the...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of the American Statistical Association|
|01_Mueller_Outlier_Robust_Model_Selection_2005.pdf||352.76 kB||Adobe PDF||Request a copy|
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