Multivariate probability density deconvolution for stationary random processes

The kernel-type estimation of the joint probability density functions of stationary random processes from noisy observations is considered. Precise asymptotic expressions and bounds on the mean-square estimation error are established, along with rates of mean-square convergence, for processes satisfying a variety of mixing conditions. The dependence of the convergence rates on the joint density of the noise process is studied.<
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