Abstract
We consider the estimation of the multivariate probability density functions of stationary random processes from noisy observations. The strong consistency and almost sure convergence rates for kernel-type deconvolution estimators is established for strongly mixing processes. The dependence of the a.s. convergence rates on the noise distribution is examined; both ordinary and super smooth noise distributions are considered.
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