Nonparametric recursive method for kernel-type function estimators for spatial data

DOI10.1016/j.spl.2018.03.017zbMath1463.62111OpenAlexW2796392699WikidataQ57519927 ScholiaQ57519927MaRDI QIDQ1642437

Yousri Slaoui, Salim Bouzebda

Publication date: 20 June 2018

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2018.03.017

zbMATH Keywords

central limit theorembandwidth selectionspatial dataregression estimationstochastic approximation algorithm

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Density estimation (62G07) Central limit and other weak theorems (60F05) Stochastic approximation (62L20)

Related Items (4)

Bandwidth selector for nonparametric recursive density estimation for spatial data defined by stochastic approximation method ⋮ Kernel regression estimation with errors-in-variables for random fields ⋮ Methodology for nonparametric bias reduction in kernel regression estimation ⋮ Large and moderate deviation principles for recursive kernel estimators of a regression function for spatial data defined by stochastic approximation method

Uses Software

• KernSmooth

Cites Work

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• On Estimation of a Probability Density Function and Mode
• An empirical process approach to the uniform consistency of kernel-type function estimators
• Combinatorial methods in density estimation

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