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

Adaptive density estimation on bounded domains under mixing conditions

Abstract : In this article, we propose a new adaptive estimator for compact supported density functions, in the framework of multivariate mixing processes. Several procedures have been proposed in the literature to tackle the boundary bias issue encountered using classical kernel estimators on the unit d-dimensional hypercube. We extend such results to more general bounded domains in R d. We introduce a specific family of kernel-type estimators adapted to the estimation of compact supported density functions. We then propose a data-driven Goldenshluger and Lepski type procedure to jointly select a kernel and a bandwidth. We prove the optimality of our procedure in the adaptive framework, stating an oracle-type inequality. We illustrate the good behavior of our new class of estimators on simulated data. Finally, we apply our procedure to a real dataset. MSC 2010 subject classifications: Primary 62G05, 62G07, 60K35.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

Cited literature [41 references]  Display  Hide  Download
Contributor : Clémentine Prieur <>
Submitted on : Monday, November 26, 2018 - 1:00:31 PM
Last modification on : Friday, July 10, 2020 - 7:58:56 AM
Long-term archiving on: : Wednesday, February 27, 2019 - 2:06:39 PM


Files produced by the author(s)


  • HAL Id : hal-01934913, version 1


Karine Bertin, Nicolas Klutchnikoff, José León, Clémentine Prieur. Adaptive density estimation on bounded domains under mixing conditions. 2018. ⟨hal-01934913v1⟩



Record views


Files downloads