M-Regression Estimation with the k Nearest Neighbor's Smoothing Under Quasi-associated Data in Functional Statistics

被引：0

作者：

Nadjet, Bellatrach ^{[1
]}

Wahiba, Bouabsa ^{[1
]}

Kadi, Attouch Mohammed ^{[1
]}

Omar, Fetitah ^{[1
]}

机构：

[1] Univ Djillali Liabes Sidi Bel Abbes, Lab Stat Stochast Proc, Sidi Bel Abbes 22000, Algeria

来源：

APPLICATIONS AND APPLIED MATHEMATICS-AN INTERNATIONAL JOURNAL | 2022年 / 17卷 / 02期

关键词：

kNN method; Functional data; Quasi-associated data; M-Regression; ROBUST NONPARAMETRIC REGRESSION; PROBABILITY DENSITY-ESTIMATION; RANDOM-VARIABLES; CONSISTENCY; CONVERGENCE; TRUNCATION;

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The main goal of this paper is to study the non parametric M-estimation under quasi-associated sequence with the k Nearest Neighbor's method shortly (kNN). We construct an estimator of this nonparametric function and we study its asymptotic properties. Furthermore, a comparison study based on simulated data is also provided to illustrate the highly sensitive of the kNN approach to the presence of even a small proportion of outliers in the data.

引用

页码：333 / 365

页数：34

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