Under finite moments and a local envelope growth condition, the least squares estimator in nonparametric regression can achieve minimax rates with heavy-tailed, covariate-dependent errors.
Minimax estimation of norms of a probability density: II. Rate-optimal estimation procedures
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In this paper we develop rate--optimal estimation procedures in the problem of estimating the $L_p$--norm, $p\in (0, \infty)$ of a probability density from independent observations. The density is assumed to be defined on $R^d$, $d\geq 1$ and to belong to a ball in the anisotropic Nikolskii space. We adopt the minimax approach and construct rate--optimal estimators in the case of integer $p\geq 2$. We demonstrate that, depending on parameters of Nikolskii's class and the norm index $p$, the risk asymptotics ranges from inconsistency to $\sqrt{n}$--estimation. The results in this paper complement the minimax lower bounds derived in the companion paper \cite{gl20}.
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On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors
Under finite moments and a local envelope growth condition, the least squares estimator in nonparametric regression can achieve minimax rates with heavy-tailed, covariate-dependent errors.