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We propose a novel approach, Sequential Lasso, for feature selection inlinear regression models with ultra-high dimensional feature spaces. Weinvestigate in this article the asymptotic properties of Sequential Lasso andestablish its selection consistency. Like other sequential methods, theimplementation of Sequential Lasso is not limited by the dimensionality of thefeature space. It has advantages over other sequential methods. The simulationstudies comparing Sequential Lasso with other sequential methods are reported.

Sequential Lasso for feature selection with ultra-high dimensional feature space