Risk upper bounds for RKHS ridge group sparse estimator in the regression model with non-Gaussian and non-bounded error
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We consider the problem of estimating a meta-model of an unknown regression model with non-Gaussian and non-bounded error. The meta-model belongs to a reproducing kernel Hilbert space constructed as a direct sum of Hilbert spaces leading to an additive decomposition including the variables and interactions between them. The estimator of this meta-model is calculated by minimizing an empirical least-squares criterion penalized by the sum of the Hilbert norm and the empirical L^2-norm. In this context, the upper bounds of the empirical L^2 risk and the L^2 risk of the estimator are established.
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