Adaptive Estimation of a Density Function using Beta Kernels

Karine Bertin () and Nicolas Klutchnikoff ()
Additional contact information
Karine Bertin: CIMFAV, Universidad de Valparaiso
Nicolas Klutchnikoff: CREST-ENSAI, Université de Strasbourg

No 2014-08, Working Papers from Center for Research in Economics and Statistics

Abstract: In this paper we are interested in the estimation of a density—defined on a compact interval of R—from n independent and identically distributed observations. In order to avoid boundary effect, beta kernel estimators are used and we propose a procedure (inspired by Lepski’s method) in order to select the bandwidth. Our procedure is proved to be adaptive in an asymptotically minimax framework. Our estimator is compared with both the cross-validation algorithm and the oracle estimator using simulated data

Pages: 22
Date: 2014-03
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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