Multivariate wavelet density and regression estimators for stationary and ergodic discrete time processes: Asymptotic results

DOI10.1080/03610926.2015.1019144zbMath1360.62155OpenAlexW2191110921MaRDI QIDQ2979611

Salim Bouzebda, Sultana Didi

Publication date: 25 April 2017

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2015.1019144

zbMATH Keywords

ergodicitystationaritymultivariate density estimationmultivariate regression estimationrates of strong convergenceMartingale differencesWavelet-based estimators

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Martingales with discrete parameter (60G42) Nonparametric estimation (62G05) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Martingales and classical analysis (60G46)

Related Items (9)

Some Characteristics of the Conditional Set-Indexed Empirical Process Involving Functional Ergodic Data ⋮ Nonlinear wavelet-based estimation to spectral density for stationary non-Gaussian linear processes ⋮ Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications ⋮ Multiresolution approximation and consistent estimation of a multivariate density function ⋮ 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 ⋮ Asymptotics for function derivatives estimators based on stationary and ergodic discrete time processes

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