Abstract
We consider the estimation of multivariate regression functions of stationary random processes with errors-in-variables. The strong consistency and uniform convergence rates, over compact sets, are established for kernel-type deconvolution estimators and strongly mixing processes. The dependence of the convergence rates on the errors distributions is examined; both ordinary and super smooth error distributions are considered.
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