Optimal model selection for density estimation of stationary data under various mixing conditions release_6k2bequsrffmlhlzb2ylb3ntpq

by Matthieu Lerasle

Released as a report .

2010  

Abstract

We propose a block-resampling penalization method for marginal density estimation with nonnecessary independent observations. When the data are β or τ-mixing, the selected estimator satisfies oracle inequalities with leading constant asymptotically equal to 1. We also prove in this setting the slope heuristic, which is a data-driven method to optimize the leading constant in the penalty.
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Type  report
Stage   submitted
Date   2010-10-09
Version   v3
Language   en ?
Number  IMS-AOS-AOS888
arXiv  0911.1497v3
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