Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications

DOI10.1080/03610926.2020.1805466OpenAlexW3049821016MaRDI QIDQ5079799

Salim Bouzebda, Sultana Didi

Publication date: 30 May 2022

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

Full work available at URL: https://doi.org/10.1080/03610926.2020.1805466

zbMATH Keywords

predictionmartingale differencesconditional densityergodic processeskernel estimatecontinuous time processesNadaraya-Watson estimatorsfunction derivatives

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Nonparametric estimation (62G05) Central limit and other weak theorems (60F05) Probability distributions: general theory (60E05) Probabilistic measure theory (60A10)

Related Items (4)

Rates of the strong uniform consistency for the kernel-type regression function estimators with general kernels on manifolds ⋮ Multivariate wavelet estimators for weakly dependent processes: strong consistency rate ⋮ Nonparametric recursive estimation for multivariate derivative functions by stochastic approximation method ⋮ Asymptotic distribution of the wavelet-based estimators of multivariate regression functions under weak dependence

Uses Software

• fda (R)
• KernSmooth
• longmemo

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