Estimation of the Density of a Determinantal Process

Mathematics – Statistics Theory

Scientific paper

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Scientific paper

We consider the problem of estimating the density {\Pi} of a determinantal process N from the observation of n independent copies of it. We use an aggregation procedure based on robust testing to build our estimator. We establish non-asymptotic risk bounds with respect to the Hellinger loss and deduce, when n goes to infinity, uniform rates of convergence over classes of densities {\Pi} of interest.

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