# On cross-validated Lasso

In this paper, we derive a rate of convergence of the Lasso estimator when the penalty parameter λ for the estimator is chosen using *K*-fold cross-validation; in particular, we show that in the model with Gaussian noise and under fairly general assumptions on the candidate set of values of λ, the prediction norm of the estimation error of the cross-validated Lasso estimator is with high probability bounded from above up-to a constant by (*s* log *p/n*)^{1/2} (log^{7/8}*n*) as long as *p* log *n/n* = *o*(1) and some other mild regularity conditions are satisfied where* n* is the sample size of available data, *p* is the number of covariates, and *s* is the number of non-zero coefficients in the model. Thus, the cross-validated Lasso estimator achieves the fastest possible rate of convergence up-to the logarithmic factor log^{7/8} *n*. In addition, we derive a sparsity bound for the cross-validated Lasso estimator; in particular, we show that under the same conditions as above, the number of non-zero coefficients of the estimator is with high probability bounded from above up-to a constant by *s* log^{5} *n*. Finally, we show that our proof technique generates non-trivial bounds on the prediction norm of the estimation error of the cross-validated Lasso estimator even if *p* is much larger than *n* and the assumption of Gaussian noise fails; in particular, the prediction norm of the estimation error is with high-probability bounded from above up-to a constant by (*s* log^{2}(*pn*) / n)^{1/4} under mild regularity conditions.