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