Large and moderate deviation principles for recursive kernel estimators of a regression function for spatial data defined by stochastic approximation method

DOI10.1016/j.spl.2019.03.007zbMath1427.62027OpenAlexW2926316360WikidataQ128190733 ScholiaQ128190733MaRDI QIDQ2322619

Yousri Slaoui, Salim Bouzebda

Publication date: 5 September 2019

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

Full work available at URL: https://doi.org/10.1016/j.spl.2019.03.007

zbMATH Keywords

nonparametric regressionstochastic approximation algorithmlarge and moderate deviation principles

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Stochastic approximation (62L20) Large deviations (60F10)

Related Items (1)

Large and moderate deviation principles for recursive kernel estimators of a regression function for spatial data defined by stochastic approximation method

Uses Software

• spatial

Cites Work

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