Abstract
We propose new nonparametric accordance Rényi- and -Tsallis divergence estimators for continuous distributions. We discuss this approach with a view to the selection model (on alétoire and autoregressive AR (1)). We lestimateur used by kernel density esttimer underlying. Nevertheless, we are able to prove that the estimators are consistent under certain conditions. We also describe how to apply these estimators and demonstrate their effectiveness through numerical experiments.
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