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This paper derives consistency results for estimation in the finite direct sum of reproducing kernel Hilbert spaces (RKHS) for dependent data. The link between penalized and constrained estimation is established. We consider the relation between topological equivalent norms for direct sums of RKHS. These norms have different implications for estimation. Estimation in a ball of the RKHS defined by these norms essentially results in estimation with a ridge and Lasso penalty, respectively. A greedy algorithm for the solution of the estimation problem under these two norms is discussed for general loss functions. [ABSTRACT FROM AUTHOR]

We consider the high-dimensional discriminant analysis problem. For this problem, different methods have been proposed and justified by establishing exact convergence rates for the classification risk, as well as the \ell 2 convergence results to the discriminative rule. However, sharp theoretical analysis for the variable selection performance of these procedures have not been established, even though model interpretation is of fundamental importance in scientific data analysis. This paper bridges the gap by providing sharp sufficient conditions for consistent variable selection using the sparse discriminant analysis. Through careful analysis, we establish rates of convergence that are significantly faster than the best known results and admit an optimal scaling of the sample size $n$ , dimensionality $p$ , and sparsity level $s$ in the high-dimensional setting. Sufficient conditions are complemented by the necessary information theoretic limits on the variable selection problem in the context of high-dimensional discriminant analysis. Exploiting a numerical equivalence result, our method also establish the optimal results for the ROAD estimator and the sparse optimal scoring estimator. Furthermore, we analyze an exhaustive search procedure, whose performance serves as a benchmark, and show that it is variable selection consistent under weaker conditions. Extensive simulations demonstrating the sharpness of the bounds are also provided. [ABSTRACT FROM AUTHOR]