Abstract
Consider a $p$-times differentiable unknown regression function $\\theta$ of a $d$-dimensional measurement variable. Let $T(\\theta)$ denote a derivative of $\\theta$ of order $m$ and set $r = (p - m)/(2p + d)$. Let $\\hat{T}_n$ denote an estimator of $T(\\theta)$ based on a training sample of size $n$, and let $\\| \\hat{T}_n - T(\\theta)\\|_q$ be the usual $L^q$ norm of the restriction of $\\hat{T}_n - T(\\theta)$ to a fixed compact set. Under appropriate regularity conditions, it is shown that the optimal rate of convergence for $\\| \\hat{T}_n - T(\\theta)\\|_q$ is $n^{-r}$ if $0 < q < \\infty$; while $(n^{-1} \\log n)^r$ is the optimal rate if $q = \\infty$.
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Publication Info
- Year
- 1982
- Type
- article
- Volume
- 10
- Issue
- 4
- Citations
- 1477
- Access
- Closed
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Identifiers
- DOI
- 10.1214/aos/1176345969