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We want to recover a signal based on noisy inhomogeneous data (the
amount of data can vary strongly on the estimation domain). We model
the data using nonparametric regression with random design, and we
focus on the estimation of the regression at a fixed point
with little, or much data. We propose a method which adapts both to
the local amount of data (the design density is unknown) and to the
local smoothness of the regression function. The procedure consists
of a local...
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