Asymptotic analysis of high-dimensional LAD regression with Lasso

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Convergence rates of least squares regression estimators with heavy-tailed errors (2017, methodology): “ion setting. In fact, we can design simple robust procedures to outperform the LSE in terms of the rate of convergence. To see this, consider the least-absolute-deviation(LAD) estimator f˜n (see e.g. [27, 59, 60], or page 336 of [77]) deﬁned by f˜ n = argminf∈F 1 n Pn i=1|Yi− f(Xi)|. It follows from”