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

作     者：Bouzebda, Salim Slaoui, Yousri 

作者机构：Univ Technol Compiegne LMAC Compiegne France Univ Poitiers Lab Math & Appl Futuroscope Chasseneuil Poitiers France 

出 版 物：《STATISTICS & PROBABILITY LETTERS》 (统计学与概率论通讯)

年 卷 期：2019年第151卷

页      面：17-28页

核心收录：

学科分类：07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：GDR 3477 GeoSto 

主　　题：Nonparametric regression Stochastic approximation algorithm Large and moderate deviation principles 

摘      要：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. (C) 2019 Elsevier B.V. All rights reserved.