ON NONPARAMETRIC CLASSIFICATION FOR WEAKLY DEPENDENT FUNCTIONAL PROCESSES

被引：5
|
作者：
Younso, Ahmad [1 ]
机构：
[1] Damascus Univ, Dept Math Stat, Fac Sci, Damascus, Syria
来源：
ESAIM-PROBABILITY AND STATISTICS | 2018年 / 21卷
关键词：
Bayes rule; training data; moving window rule; mixing condition; consistency; CENTRAL-LIMIT-THEOREM; REGRESSION; CONVERGENCE;
D O I：
10.1051/ps/2017002
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
The purpose of this paper is to investigate the moving window rule of classification to classify functions under mixing conditions. We consider a random variable X taking values in a metric space (F, rho) with label Y is an element of{0, 1}. We extend some results on consistency and strong consistency of the moving window rule from the i.i.d. case to the weakly dependent case under mild assumptions. The practical use of the moving window rule will be illustrated through a simulation study. The performance of the moving window rule is investigated.
引用
收藏
页码：452 / 466
页数：15
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