|Title||Consistent Group Identification and Variable Selection in Regression with Correlated Predictors.|
|Publication Type||Journal Article|
|Year of Publication||2013|
|Authors||Sharma, Dhruv B., Howard D. Bondell, and Hao Helen Zhang|
|Journal||J Comput Graph Stat|
|Date Published||2013 Apr 01|
Statistical procedures for variable selection have become integral elements in any analysis. Successful procedures are characterized by high predictive accuracy, yielding interpretable models while retaining computational efficiency. Penalized methods that perform coefficient shrinkage have been shown to be successful in many cases. Models with correlated predictors are particularly challenging to tackle. We propose a penalization procedure that performs variable selection while clustering groups of predictors automatically. The oracle properties of this procedure including consistency in group identification are also studied. The proposed method compares favorably with existing selection approaches in both prediction accuracy and model discovery, while retaining its computational efficiency. Supplemental material are available online.
|Alternate Journal||J Comput Graph Stat|
|Original Publication||Consistent group identification and variable selection in regression with correlated predictors.|
|PubMed Central ID||PMC3678393|
|Grant List||P01 CA134294 / CA / NCI NIH HHS / United States |
P01 CA142538 / CA / NCI NIH HHS / United States
R01 CA085848 / CA / NCI NIH HHS / United States
R01 MH084022 / MH / NIMH NIH HHS / United States
Consistent Group Identification and Variable Selection in Regression with Correlated Predictors.