RR-classifier: a nonparametric classification procedure in multidimensional space based on relative ranks

IF 1.4 4区 数学 Q2 STATISTICS & PROBABILITY Asta-Advances in Statistical Analysis Pub Date : 2021-10-21 DOI:10.1007/s10182-021-00423-7
Ondrej Vencalek, Olusola Samuel Makinde
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引用次数: 1

Abstract

Notions of data depth have motivated nonparametric multivariate analysis, especially in supervised learning. Maximum depth classifiers, classifiers based on depth-depth plots and depth distribution classifiers are nonparametric classification methodologies based on the notions of data depth and are Bayes-optimal rule under certain conditions. This paper proposes rank-rank plot for classification. Theoretical properties of the suggested classifier are investigated in some particular cases given by specific distributional assumptions. The performance of the proposed classification method is further investigated using simulated datasets.

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RR分类器:一种基于相对秩的多维空间非参数分类方法
数据深度的概念推动了非参数多变量分析,尤其是在监督学习中。最大深度分类器、基于深度-深度图的分类器和深度分布分类器是基于数据深度概念的非参数分类方法,在一定条件下是贝叶斯最优规则。本文提出了用于分类的秩秩图。在特定分布假设给出的一些特定情况下,研究了所提出分类器的理论性质。使用模拟数据集进一步研究了所提出的分类方法的性能。
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来源期刊
Asta-Advances in Statistical Analysis
Asta-Advances in Statistical Analysis 数学-统计学与概率论
CiteScore
2.20
自引率
14.30%
发文量
39
审稿时长
>12 weeks
期刊介绍: AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.
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