The k-nearest neighbors method in single index regression model for functional quasi-associated time series data

被引：0
|
作者：
Salim Bouzebda
Ali Laksaci
Mustapha Mohammedi
机构：
[1] Université de technologie de Compiègne,LMAC (Laboratory of Applied Mathematics of Compiègne)
[2] King Khalid University,Department of Mathematics, College of Science
[3] Université Djillali Liabès,undefined
[4] L.S.P.S.,undefined
[5] Université Abdelhamid Ibn Badis de Mostaganem,undefined
来源：
Revista Matemática Complutense | 2023年 / 36卷
关键词：
Single functional index model; -Nearest Neighbors; Functional Hilbert space; Kernel regression estimation; Weak dependence; Quasi-associated variables; Almost complete convergence rates; 62G05; 62G08; 62L12; 62G20;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In the present paper, we consider the k-Nearest Neighbors (k-NN) method in the single index regression model in the case of a functional predictor and a scalar response. The main result of this work is the establishment of the almost complete convergence rates for the quasi-associated data. The obtained results rely on the classical functional kernel estimate. Some simulation studies are carried out to show the finite sample performances of the k-NN estimator. Finally, we show how to use the k-NN estimator of the functional single index regression model in the analysis of air quality to illustrate the effectiveness of our methodology.
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页码：361 / 391
页数：30
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