Model selection for (auto-)regression with dependent data

Baraud, Yannick; Comte, F.; Viennet, G.

ESAIM: Probability and Statistics, Tome 5 (2001), pp. 33-49.

In this paper, we study the problem of non parametric estimation of an unknown regression function from dependent data with sub-gaussian errors. As a particular case, we handle the autoregressive framework. For this purpose, we consider a collection of finite dimensional linear spaces (e.g. linear spaces spanned by wavelets or piecewise polynomials on a possibly irregular grid) and we estimate the regression function by a least-squares estimator built on a data driven selected linear space among the collection. This data driven choice is performed via the minimization of a penalized criterion akin to the Mallows’ $C_p$. We state non asymptotic risk bounds for our estimator in some ${Ł}_2$-norm and we show that it is adaptive in the minimax sense over a large class of Besov balls of the form ${ℬ}_{\alpha ,p,\infty }\left(R\right)$ with $p\ge 1$.

Classification : 62G08, 62J02
Mots clés : nonparametric regression, least-squares estimator, adaptive estimation, autoregression, mixing processes

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