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Convergence rates of kernel density estimators for stationary time series arewell studied. For invertible linear processes, we construct a new densityestimator that converges, in the supremum norm, at the better, parametric, rate$n^{-1/2}$. Our estimator is a convolution of two different residual-basedkernel estimators. We obtain in particular convergence rates for suchresidual-based kernel estimators; these results are of independent interest.

Uniformly root-$N$ consistent density estimators for weakly dependent invertible linear processes