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Adaptive density estimation for stationary processes

Authors
  • Lerasle, Matthieu
Type
Published Article
Publication Date
Sep 05, 2009
Submission Date
Sep 05, 2009
Identifiers
DOI: 10.3103/S1066530709010049
Source
arXiv
License
Yellow
External links

Abstract

We propose an algorithm to estimate the common density $s$ of a stationary process $X_1,...,X_n$. We suppose that the process is either $\beta$ or $\tau$-mixing. We provide a model selection procedure based on a generalization of Mallows' $C_p$ and we prove oracle inequalities for the selected estimator under a few prior assumptions on the collection of models and on the mixing coefficients. We prove that our estimator is adaptive over a class of Besov spaces, namely, we prove that it achieves the same rates of convergence as in the i.i.d framework.

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