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Adaptive estimation of the stationary density of discrete and continuous time mixingprocesses

Published online by Cambridge University Press:  15 November 2002

Fabienne Comte  and
Florence Merlevède
Fabienne Comte
Affiliation:
Université Paris V, Laboratoire MAP5, 45 rue des Saints-Pères, 75270 Paris Cedex 06, France; comte@biomedicale.univ-paris5.fr.
Florence Merlevède
Affiliation:
Université Paris VI, LSTA, 4 place Jussieu, 75252 Paris Cedex 05, France; merleve@ccr.jussieu.fr.
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Abstract

In this paper, we study the problem of non parametric estimation of the stationary marginal density f of an α or a β-mixing process, observed either in continuous time or in discrete time. We present an unified framework allowing to deal with many different cases. We consider a collection of finite dimensional linear regular spaces. We estimate f using a projection estimator built on a data driven selected linear space among the collection. This data driven choice is performed via the minimization of a penalized contrast. We state non asymptotic risk bounds, regarding to the integrated quadratic risk, for our estimators, in both cases of mixing. We show that they are adaptive in the minimax sense over a large class of Besov balls. In discrete time, we also provide a result for model selection among an exponentially large collection of models (non regular case).

Keywords

Non parametric estimationprojection estimatoradaptive estimationmodel selectionmixing processescontinuous timediscrete time.
Type
Research Article
Information
ESAIM: Probability and Statistics , Volume 6: New directions in Time Series Analysis (Guest Editor: Philippe Soulier)New directions in Time Series Analysis (Guest Editor: Philippe Soulier) , 2002 , pp. 211 - 238
Copyright
© EDP Sciences, SMAI, 2002

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