Statistica Sinica 22 (2012), 1021-1040
ROBUST
COMBINATION OF MODEL SELECTION
METHODS FOR PREDICTION
Xiaoqiao Wei and Yuhong Yang
University of Minnesota
Abstract: One important goal of regression analysis
is prediction. In recent years, the idea of combining different
statistical methods has attracted an increasing attention. In this
work, we propose a method, 
-ARM (adaptive regression by
mixing), to robustly combine model selection methods that performs
well adaptively. In numerical work, we consider the LASSO, SCAD, and
adaptive LASSO in representative scenarios, as well as in cases of
randomly generated models. The -ARM automatically performs like
the best among them and consequently provides a better
estimation/prediction in an overall sense, especially when outliers
are likely to occur.
Key words and
phrases: Adaptive LASSO, ARM, combining model selection methods,
LASSO, SCAD.