Nonparametric Estimation for Stationary Processes


We consider the kernel density and regression estimation problem for a wide class of causal processes. Asymptotic normality of the kernel estimators is established under minimal regularity conditions on bandwidths. Optimal uniform error bounds are obtained without imposing strong mixing conditions. The proposed method is based on martingale approximations and provides a unified framework for nonparametric time series analysis, and enables one to launch a systematic study for dependent observations.

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@inproceedings{Wu2003NonparametricEF, title={Nonparametric Estimation for Stationary Processes}, author={Wei Biao Wu}, year={2003} }