Asymptotic equivalence for nonparametric regression with multivariate and random design

Markus Reiß

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Asymptotic equivalence for nonparametric regression with multivariate and random design

Markus Reiß

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Abstract

We show that nonparametric regression is asymptotically equivalent in Le Cam's sense with a sequence of Gaussian white noise experiments as the number of observations tends to infinity. We propose a general constructive framework based on approximation spaces, which permits to achieve asymptotic equivalence even in the cases of multivariate and random design.

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Markus Reiß. 2006-07-14. Asymptotic equivalence for nonparametric regression with multivariate and random design. https://arxiv.org/abs/math/0607342

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