Optimal asymptotic quadratic error of nonparametric regression function estimates for a continuous-time process from sampled-data

DOI10.1080/02331889908802665zbMath0916.62034OpenAlexW1979530211MaRDI QIDQ4235727

Nathalie Chèze-Payaud, Denis Bosq

Publication date: 4 July 1999

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331889908802665

zbMATH Keywords

nonparametric regressionsampled datachoice of bandwidthquadratic-mean convergencemixing continuous-parameter processes

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Non-Markovian processes: estimation (62M09) General nonlinear regression (62J02)

Related Items (13)

Recursive estimation of nonparametric regression with functional covariate ⋮ Recursive regression estimators with application to nonparametric prediction ⋮ Local Hölder exponent estimation for multivariate continuous time processes ⋮ Asymptotic normality of recursive estimators under strong mixing conditions ⋮ Consistency of the recursive nonparametric regression estimation for dependent functional data ⋮ Nonparametric semirecursive identification in a wide sense of strong mixing processes ⋮ Semi-recursive nonparametric identification in the general sense of a nonlinear heteroscedastic autoregression ⋮ Asymptotic normality of kernel density function estimator from continuous time stationary and dependent processes ⋮ Optimal asymptotic quadratic error of kernel estimators of Radon–Nikodym derivatives for strong mixing data ⋮ Adaptive sampling schemes for density estimation ⋮ Optimal asymptotic MSE of kernel regression estimate for continuous time processes with missing at random response ⋮ Theory for high-order bounds in functional principal components analysis ⋮ Consistency of recursive nonparametric Kernel estimates for independent functional data

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