Abstract High dimensional data are nowadays encountered in various branches of science. Variable selection techniques play a key role in analyzing high dimensional data. Generally two approaches for variable selection in the high dimensional data setting are considered--forward selection methods and penalization methods. In the former, variables are introduced in the model one at a time depending on their ability to explain variation and the procedure is terminated at some stage following some .
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