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Raphaël Coudret, Gilles Durrieu, Jerome Saracco. A note about the critical bandwidth for a kernel density estimator with the uniform kernel.. Among available bandwidths for

For sake of succintness, we do not give all these simulations results here, but focuse on the construction of confidence ellipsoid for (θ, µ); the aim of this example is of course

We define the kernel estimator and state the main results on the estimation of the density of µ, see Lemma 3.5 (consistency) and Theorem 3.6 (asymptotic normality), in Section 3.1.

Lastly, we propose a model selection strategy relying on a Goldenschluger and Lepski [21] method and prove a risk bound for the nal estimator: this result holds for

It has been pointed out in a couple of recent works that the weak convergence of its centered and rescaled versions in a weighted Lebesgue L p space, 1 ≤ p < ∞, considered to be

This section introduces a multiplicative adjustment to a pilot kernel estimator that reduces the bias without, asymptotically, changing the variance.. Because the pilot

To conclude, asymptotic properties of ˆ f K,h crit with the Gaussian kernel, together with its behavior in simulation, and its deterministic number of modes allow this estimator to

Keywords: Reflected Brownian motion ; Brownian bridge ; Brownian excursions ; local score ; sequence analysis.. 2000 MSC: 60 G 17 ; 60 J 25 ; 60