Deconvolving a density from contaminated dependent observations

The paper studies the performance of deconvoluting kernel density estimators for estimating the marginal density of a linear process. The data stem from the linear process and are partially, respectively fully contaminated by iid errors with a known distribution. If 1–p denotes the proportion of contaminated observations (and it is, of course, unknown which observations are contaminated and which are not) then for 1–p (0, 1) and under mild conditions almost sure deconvolution rates of orderO(n ^–2/5(logn)^9/10) can be achieved for convergence in . This rate compares well with the existing rates foriid uncontaminated observations. Forp=0 and exponentially decreasing error characteristic function the corresponding rates are of merely logarithmic order. As a by-product the paper also gives a rate of convergence result for the empirical characteristic function in the linear process context and utilizes this to demonstrate that deconvoluting kernel density estimators attain the optimal rate in the dependence case with exponentially decreasing error characteristic function.This work was partially supported by a grant from the Deutsche Forschungsgemeinschaft.