Adaptation on the space of finite signed measures |
| |
| Authors: | E Giné R Nickl |
| |
| Institution: | (1) Dept. of Math., University of Connecticut, Connecticut, USA |
| |
| Abstract: | Given an i.i.d. sample from a probability measure P on ℝ, an estimator is constructed that efficiently estimates P in the bounded-Lipschitz metric for weak convergence of probability measures, and, at the same time, estimates the density
of P — if it exists (but without assuming it does) — at the best possible rate of convergence in total variation loss (that is,
in L
1-loss for densities).
|
| |
| Keywords: | kernel density estimator exponential inequality adaptive estimation total variation loss bounded Lipschitz metric L 1-loss |
| 本文献已被 SpringerLink 等数据库收录！ |
|
