Model selection for (auto-)regression with dependent data
Yannick Baraud, F. Comte, G. Viennet (2010)
ESAIM: Probability and Statistics
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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 ( 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...