Model Selection in Gaussian Regression for High-dimensional Data


We consider model selection in Gaussian regression, where the number of predictors might be even larger than the number of observations. The proposed procedure is based on penalized least square criteria with a complexity penalty on a model size. We discuss asymptotic properties of the resulting estimators corresponding to linear and so-called 2k ln(p/k… (More)


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