Asymptotic normality of kernel estimates in a regression model for random fields

DOI10.1080/10485250903505893zbMath1203.62065arXiv0907.1519OpenAlexW2022815333MaRDI QIDQ3068115

Mohamed El Machkouri, Radu S. Stoica

Publication date: 13 January 2011

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0907.1519

zbMATH Keywords

asymptotic normalitykernel estimatornonparametric regression estimationstrongly mixing random field

Mathematics Subject Classification ID

Random fields (60G60) Random fields; image analysis (62M40) Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05)

Related Items (11)

Limit theorems for linear random fields with innovations in the domain of attraction of a stable law ⋮ On local linear regression for strongly mixing random fields ⋮ CLT for linear random fields with stationary martingale-difference innovation ⋮ Nonparametric relative error regression for spatial random variables ⋮ Asymptotic normality of the Parzen-Rosenblatt density estimator for strongly mixing random fields ⋮ On nonparametric inference for spatial regression models under domain expanding and infill asymptotics ⋮ Exact moderate and large deviations for linear random fields ⋮ Nonparametric prediction of spatial multivariate data ⋮ On the Nadaraya-Watson kernel regression estimator for irregularly spaced spatial data ⋮ Convergence rates in the central limit theorem for weighted sums of Bernoulli random fields ⋮ A new spatial regression estimator in the multivariate context

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