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

Salim Bouzebda and Yousri Slaoui

Statistics & Probability Letters, 2019, vol. 151, issue C, 17-28

Abstract: In the present paper, we are mainly concerned with a family of kernel type estimators based upon spatial data. More precisely, we establish large and moderate deviations principles for the recursive kernel estimators of a regression function for spatial data defined by the stochastic approximation algorithm.

Keywords: Nonparametric regression; Stochastic approximation algorithm; Large and moderate deviation principles (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:151:y:2019:i:c:p:17-28

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DOI: 10.1016/j.spl.2019.03.007

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