Asymptotic equivalence for nonparametric regression with non-regular errors
by Alexander Meister
Abstract: In this talk the LeCam theory for nonparametric regression experiments is extended to the case of non-regular error densities, which have jump discontinuities at their endpoints. We prove asymptotic equivalence of such regression models and two simultaneously observed independent Poisson point processes which contain the target curve as the support boundary of their intensity functions. That model significantly differs from regression problems with normal or regular errors, which are known to be asymptotically equivalent to Gaussian white noise models. This talk is based on a joint work with M. Reiss (Humboldt-University of Berlin).
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