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Multivariate intensity estimation via hyperbolic wavelet selection

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STS - Statistical scalability

We propose a new statistical procedure that can in some sense overcome the curse of dimensionality without structural assumptions on the function to estimate. It relies on a least-squares type penalized criterion and a new collection of models built from hyperbolic biorthogonal wavelet bases. We study its properties in a unifying intensity estimation framework, where an oracle-type inequality and adaptation to mixed dominating smoothness are shown to hold. We also explain how to implement the estimator with an algorithm whose complexity is manageable. (Reference: JMVA , Volume 161, p.32-57)

This talk is part of the Isaac Newton Institute Seminar Series series.

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