×

zbMATH — the first resource for mathematics

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
PDF BibTeX XML Cite