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Sparse estimation for the drift of high-dimensional Ornstein--Uhlenbeck processes with i.i.d. paths
We study sparsity-regularized maximum likelihood estimation for the drift parameter of high-dimensional non-stationary Ornstein--Uhlenbeck processes given repeated measurements of i.i.d. paths. In particular, we show that Lasso and Slope estimators can achieve the minimax optimal rate of convergence
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