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In the context of a linear model with a sparse coefficient vector, exponential weights methods have been shown to be achieve oracle inequalities for prediction. We show that such methods also succeed ...
Penalized regression models are popularly used in high-dimensional data analysis to conduct variable selection and model fitting simultaneously. Whereas success has been widely reported in literature,...
This paper investigates correct variable selection in finite samples via $ell_1$ and $ell_1 + ell_2$ type penalization schemes. The asymptotic consistency of variable selection immediately follows fro...
Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatica...
When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The ...
For multivariate normal models and some exponential family models a multiple testing stepwise method is offered that is both admissible and consistent. The method is readily adaptable to selecting var...

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