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May 2022 Minimax estimation of norms of a probability density: I. Lower bounds
Alexander Goldenshluger, Oleg V. Lepski
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Bernoulli 28(2): 1120-1154 (May 2022). DOI: 10.3150/21-BEJ1380

Abstract

The paper deals with the problem of nonparametric estimating the Lp–norm, p(1,), of a probability density on Rd, d1 from independent observations. The unknown density is assumed to belong to a ball in the anisotropic Nikolskii’s space. We adopt the minimax approach, and derive lower bounds on the minimax risk. In particular, we demonstrate that accuracy of estimation procedures essentially depends on whether p is integer or not. Moreover, we develop a general technique for derivation of lower bounds on the minimax risk in the problems of estimating nonlinear functionals. The proposed technique is applicable for a broad class of nonlinear functionals, and it is used for derivation of the lower bounds in the Lp–norm estimation.

Funding Statement

The first author was supported by the ISF grant No. 361/15.
This work has been carried out in the framework of the Labex Archimède (ANR-11-LABX-0033) and of the A*MIDEX project (ANR-11-IDEX-0001-02), funded by the “Investissements d’Avenir” French Government program managed by the French National Research Agency (ANR).

Acknowledgements

The authors are grateful to two anonymous referees and the Associate Editor for careful reading and useful remarks that led to significant improvements in the presentation.

Citation

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Alexander Goldenshluger. Oleg V. Lepski. "Minimax estimation of norms of a probability density: I. Lower bounds." Bernoulli 28 (2) 1120 - 1154, May 2022. https://doi.org/10.3150/21-BEJ1380

Information

Received: 1 March 2021; Revised: 1 June 2021; Published: May 2022
First available in Project Euclid: 3 March 2022

Digital Object Identifier: 10.3150/21-BEJ1380

Keywords: anisotropic Nikolskii’s class , Best approximation , estimation of nonlinear functionals , minimax estimation , minimax risk

Rights: Copyright © 2022 ISI/BS

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Vol.28 • No. 2 • May 2022
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