Multivariate wavelet density and regression estimators for stationary and ergodic continuous time processes: asymptotic results

DOI10.3103/S1066530715030011zbMath1332.62111MaRDI QIDQ258033

F. Blanchet-Sadri, M. Dambrine

Publication date: 17 March 2016

Published in: Mathematical Methods of Statistics (Search for Journal in Brave)

zbMATH Keywords

ergodicitymartingale differencesstationaritymultivariate density estimationcontinuous time processesmultivariate regression estimationrates of strong convergencewavelet-based estimators

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05) Strong limit theorems (60F15)

Related Items

Some Characteristics of the Conditional Set-Indexed Empirical Process Involving Functional Ergodic Data, Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications, Multivariate wavelet estimators for weakly dependent processes: strong consistency rate, Asymptotic distribution of the wavelet-based estimators of multivariate regression functions under weak dependence, Some asymptotic properties of kernel regression estimators of the mode for stationary and ergodic continuous time processes, Nonparametric density estimation for spatial data with wavelets, Uniform limit theorems for a class of conditional \(Z\)-estimators when covariates are functions, Asymptotics for function derivatives estimators based on stationary and ergodic discrete time processes, Multivariate wavelet density and regression estimators for stationary and ergodic continuous time processes: asymptotic results

Uses Software

• KernSmooth

Cites Work

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