Electronic Journal of Statistics

$\mathbb{L}_{p}$ adaptive estimation of an anisotropic density under independence hypothesis

Gilles Rebelles

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Abstract

In this paper, we focus on the problem of a multivariate density estimation under an $\mathbb{L}_{p}$-loss. We provide a data-driven selection rule from a family of kernel estimators and derive for it $\mathbb{L}_{p}$-risk oracle inequalities depending on the value of $p\geq1$. The proposed estimator permits us to take into account approximation properties of the underlying density and its independence structure simultaneously. Specifically, we obtain adaptive upper bounds over a scale of anisotropic Nikolskii classes when the smoothness is also measured with the $\mathbb{L}_{p}$-norm. It is important to emphasize that the adaptation to unknown independence structure of the estimated density allows us to improve significantly the accuracy of estimation (curse of dimensionality). The main technical tools used in our derivation are uniform bounds on the $\mathbb{L}_{p}$-norms of empirical processes developed in Goldenshluger and Lepski [13].

Article information

Source
Electron. J. Statist., Volume 9, Number 1 (2015), 106-134.

Dates
First available in Project Euclid: 6 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1423229752

Digital Object Identifier
doi:10.1214/15-EJS986

Mathematical Reviews number (MathSciNet)
MR3306572

Zentralblatt MATH identifier
1307.62149

Keywords
Density estimation oracle inequality adaptation independence structure upper function

Citation

Rebelles, Gilles. $\mathbb{L}_{p}$ adaptive estimation of an anisotropic density under independence hypothesis. Electron. J. Statist. 9 (2015), no. 1, 106--134. doi:10.1214/15-EJS986. https://projecteuclid.org/euclid.ejs/1423229752


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