Strong consistency of a kernel-based rule for spatially dependent data

DOI10.1016/j.ajmsc.2019.10.004zbMath1470.62099OpenAlexW2988951860MaRDI QIDQ5009834

Ahmad Younso, Ziad Kanaya, Nour Azhari

Publication date: 6 August 2021

Published in: Arab Journal of Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ajmsc.2019.10.004

zbMATH Keywords

bandwidthrandom fieldstrong consistencyBayes rulekernel rule

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Random fields; image analysis (62M40) Asymptotic properties of nonparametric inference (62G20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)

Related Items (1)

Consistency of the \(k\)-nearest neighbor classifier for spatially dependent data

Cites Work

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• Inequalities for absolutely regular sequences: application to density estimation
• Kernel density estimation for random fields. (Density estimation for random fields)
• Nonparametric spatial prediction
• Asymptotic theory of weakly dependent stochastic processes
• On the consistency of a new kernel rule for spatially dependent data
• Nonparametric prediction of spatial multivariate data
• Some Limit Theorems for Stationary Processes

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