Regression operator estimation by delta-sequences method for functional data and its applications

被引:0
|
作者
Idir Ouassou
Mustapha Rachdi
机构
[1] Université Cadi Ayyad,Ecole Nationale des Sciences Appliquées
[2] Université Pierre Mendès France (Grenoble 2),Laboratoire AGIM, FRE 3405 CNRS, TIMB Group
[3] UFR SHS,undefined
来源
AStA Advances in Statistical Analysis | 2012年 / 96卷
关键词
Nonparametric regression operator estimation; Functional data; Strong consistency; Method of delta-sequences; Measure theory on a Banach space; Small ball probabilities;
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摘要
In this paper, we introduce a somewhat more general class of nonparametric estimators (delta-sequences estimators) for estimating an unknown regression operator from noisy data. The regressor is assumed to take values in an infinite-dimensional separable Banach space, when the response variable is a scalar. Under some general conditions, we establish the uniform almost-complete convergence with the rates of these estimators. Moreover, we give some particular cases of our results, which can also be considered as novel in the finite-dimensional setting. Moreover, after giving some examples of the impact of our results, we show how to use them in some statistical applications (prediction procedure and curve discrimination).
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页码:451 / 465
页数:14
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