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math.ST2026
Deconvolution of arbitrary distribution functions and densities
Henrik Kaiser
In this article we propose a novel approach for the deconvolution of the distribution function associated with an arbitrary probability measure (and possibly existing density). We…
math.ST2025
Deconvolution of distribution functions without integral transforms
Henrik Kaiser
We study the recovery of the distribution function of a random variable that is subject to an independent additive random error . To be precise, it is assume…
math.ST2025
Mean and quantile regression in the copula setting: properties, sharp bounds and a note on estimation
Henrik Kaiser, Wolfgang Trutschnig
Driven by the interest on how uniformity of marginal distributions propa\-gates to properties of regression functions, in this contribution we tackle the following questions: Given…