Robust variable selection under cellwise contamination

IF 1.1 4区数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Statistical Computation and Simulation Pub Date : 2023-11-28 DOI:10.1080/00949655.2023.2286316

Peng Su, Garth Tarr, Samuel Muller

引用次数: 2

Abstract

Cellwise outliers are widespread in real world data analysis. Traditional robust methods may fail when applied to datasets under such contamination. We introduce a variable selection procedure, tha...

查看原文本刊更多论文

细胞污染下的鲁棒变量选择

单元格异常值在现实世界的数据分析中很普遍。传统的鲁棒方法在处理这种污染下的数据集时可能会失效。我们引入了一个变量选择程序，即…

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Statistical Computation and Simulation 数学-计算机：跨学科应用

CiteScore

2.30

自引率

8.30%

发文量

156

审稿时长

4-8 weeks

期刊介绍： Journal of Statistical Computation and Simulation ( JSCS ) publishes significant and original work in areas of statistics which are related to or dependent upon the computer. Fields covered include computer algorithms related to probability or statistics, studies in statistical inference by means of simulation techniques, and implementation of interactive statistical systems. JSCS does not consider applications of statistics to other fields, except as illustrations of the use of the original statistics presented. Accepted papers should ideally appeal to a wide audience of statisticians and provoke real applications of theoretical constructions.