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Non compact estimation of the conditional density from direct or noisy data

Abstract : In this paper, we propose a nonparametric estimation strategy for the conditional density function of Y given X, from independent and identically distributed observations (Xi, Yi) 1≤i≤n. We consider a regression strategy related to projection subspaces of L 2 generated by non compactly supported bases. This rst study is then extended to the case where Y is not directly observed, but only Z = Y + ε, where ε is a noise with known density. In these two settings, we build and study collections of estimators, compute their rates of convergence on anisotropic space on non-compact supports, and prove related lower bounds. Then, we consider adaptive estimators for which we also prove risk bounds.
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Preprints, Working Papers, ...
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Contributor : Fabienne Comte Connect in order to contact the contributor
Submitted on : Thursday, July 1, 2021 - 10:25:38 PM
Last modification on : Friday, March 25, 2022 - 5:50:02 PM
Long-term archiving on: : Saturday, October 2, 2021 - 7:17:43 PM


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  • HAL Id : hal-03276251, version 1


Fabienne Comte, Claire Lacour. Non compact estimation of the conditional density from direct or noisy data. 2021. ⟨hal-03276251⟩



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