Papers › Density estimation on an unknown submanifold
Density estimation on an unknown submanifold
Clément Berenfeld, Marc Hoffmann
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We investigate density estimation from a n-sample in the Euclidean space ℝᴰ, when the data is supported by an unknown submanifold M of possibly unknown dimension d < D under a reach condition. We study nonparametric kernel methods for pointwise loss, with data-driven bandwidths that incorporate some learning of the geometry via a local dimension estimator. When f has H\"older smoothness β and M has regularity α, our estimator achieves the rate n^(-α∧β/(2α∧β+d)) and does not depend on the ambient dimension D and is asymptotically minimax for α≥β. Following Lepski's principle, a bandwidth selection rule is shown to achieve smoothness adaptation. We also investigate the case α≤β: by estimating in some sense the underlying geometry of M, we establish in dimension d=1 that the minimax rate is n^(-β/(2β+1)) proving in particular that it does not depend on the regularity of M. Finally, a numerical implementation is conducted on some case studies in order to confirm the practical feasibility of our estimators.
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