Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Adaptive regression with Brownian path covariate

Abstract : This paper deals with estimation with functional covariates. More precisely , we aim at estimating the regression function m of a continuous outcome Y against a standard Wiener coprocess W. Following Cadre and Truquet (2015) and Cadre, Klutchnikoff, and Massiot (2017) the Wiener-Itô decomposition of m(W) is used to construct a family of estimators. The minimax rate of convergence over specific smoothness classes is obtained. A data-driven selection procedure is defined following the ideas developed by Goldenshluger and Lepski (2011). An oracle-type inequality is obtained which leads to adaptive results.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

Cited literature [26 references]  Display  Hide  Download
Contributor : Nicolas Klutchnikoff <>
Submitted on : Friday, October 25, 2019 - 9:51:49 AM
Last modification on : Thursday, April 22, 2021 - 3:20:51 AM
Long-term archiving on: : Sunday, January 26, 2020 - 1:49:20 PM


Files produced by the author(s)


  • HAL Id : hal-02332820, version 1
  • ARXIV : 1907.11284


Karine Bertin, Nicolas Klutchnikoff. Adaptive regression with Brownian path covariate. 2019. ⟨hal-02332820⟩



Record views


Files downloads