On pointwise adaptive curve estimation based on inhomogeneous data

Stéphane Gaïffas

ESAIM: Probability and Statistics (2007)

  • Volume: 11, page 344-364
  • ISSN: 1292-8100

Abstract

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We want to recover a signal based on noisy inhomogeneous data (the amount of data can vary strongly on the estimation domain). We model the data using nonparametric regression with random design, and we focus on the estimation of the regression at a fixed point x0 with little, or much data. We propose a method which adapts both to the local amount of data (the design density is unknown) and to the local smoothness of the regression function. The procedure consists of a local polynomial estimator with a Lepski type data-driven bandwidth selector, see for instance Lepski et al. [Ann. Statist.25 (1997) 929–947]. We assess this procedure in the minimax setup, over a class of function with local smoothness s > 0 of Hölder type. We quantify the amount of data at x0 in terms of a local property on the design density called regular variation, which allows situations with strong variations in the concentration of the observations. Moreover, the optimality of the procedure is proved within this framework.

How to cite

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Gaïffas, Stéphane. "On pointwise adaptive curve estimation based on inhomogeneous data." ESAIM: Probability and Statistics 11 (2007): 344-364. <http://eudml.org/doc/250082>.

@article{Gaïffas2007,
abstract = { We want to recover a signal based on noisy inhomogeneous data (the amount of data can vary strongly on the estimation domain). We model the data using nonparametric regression with random design, and we focus on the estimation of the regression at a fixed point x0 with little, or much data. We propose a method which adapts both to the local amount of data (the design density is unknown) and to the local smoothness of the regression function. The procedure consists of a local polynomial estimator with a Lepski type data-driven bandwidth selector, see for instance Lepski et al. [Ann. Statist.25 (1997) 929–947]. We assess this procedure in the minimax setup, over a class of function with local smoothness s > 0 of Hölder type. We quantify the amount of data at x0 in terms of a local property on the design density called regular variation, which allows situations with strong variations in the concentration of the observations. Moreover, the optimality of the procedure is proved within this framework. },
author = {Gaïffas, Stéphane},
journal = {ESAIM: Probability and Statistics},
keywords = {Adaptive estimation; inhomogeneous data; nonparametric regression; random design.; adaptive estimation; random design},
language = {eng},
month = {8},
pages = {344-364},
publisher = {EDP Sciences},
title = {On pointwise adaptive curve estimation based on inhomogeneous data},
url = {http://eudml.org/doc/250082},
volume = {11},
year = {2007},
}

TY - JOUR
AU - Gaïffas, Stéphane
TI - On pointwise adaptive curve estimation based on inhomogeneous data
JO - ESAIM: Probability and Statistics
DA - 2007/8//
PB - EDP Sciences
VL - 11
SP - 344
EP - 364
AB - We want to recover a signal based on noisy inhomogeneous data (the amount of data can vary strongly on the estimation domain). We model the data using nonparametric regression with random design, and we focus on the estimation of the regression at a fixed point x0 with little, or much data. We propose a method which adapts both to the local amount of data (the design density is unknown) and to the local smoothness of the regression function. The procedure consists of a local polynomial estimator with a Lepski type data-driven bandwidth selector, see for instance Lepski et al. [Ann. Statist.25 (1997) 929–947]. We assess this procedure in the minimax setup, over a class of function with local smoothness s > 0 of Hölder type. We quantify the amount of data at x0 in terms of a local property on the design density called regular variation, which allows situations with strong variations in the concentration of the observations. Moreover, the optimality of the procedure is proved within this framework.
LA - eng
KW - Adaptive estimation; inhomogeneous data; nonparametric regression; random design.; adaptive estimation; random design
UR - http://eudml.org/doc/250082
ER -

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