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Tight conditions for consistency of variable selection in the context of high dimensionality
2012
Annals of Statistics
We address the issue of variable selection in the regression model with very high ambient dimension, that is, when the number of variables is very large. The main focus is on the situation where the number of relevant variables, called intrinsic dimension, is much smaller than the ambient dimension d. Without assuming any parametric form of the underlying regression function, we get tight conditions making it possible to consistently estimate the set of relevant variables. These conditions
doi:10.1214/12-aos1046
fatcat:fb77qfkpc5c5hmkcfr622ddhhu