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Nonparametric regression and prediction for continuous-time processes. (English) Zbl 0794.62027
Summary: In order to predict a continuous-time process we introduce and study a nonparametric predictor. First we present a consistent kernel estimator $$r_ T(x)$$ for the regression function $$r(x)$$. Under some mild mixing conditions, optimal convergence and asymptotic distributional results of kernel regression estimates are obtained.

##### MSC:
 62G07 Density estimation 62M20 Inference from stochastic processes and prediction 62G20 Asymptotic properties of nonparametric inference 62E20 Asymptotic distribution theory in statistics