Robust tools for the imperfect world

被引：38
|
作者：
Filzmoser, Peter [1 ]
Todorov, Valentin [2 ]
机构：
[1] Vienna Univ Technol, Dept Stat & Probabil Theory, A-1040 Vienna, Austria
[2] Vienna Int Ctr, UN Ind Dev Org UNIDO, A-1400 Vienna, Austria
来源：
INFORMATION SCIENCES | 2013年 / 245卷
关键词：
Robustness; MCD; Outlier; High breakdown; PCA; Statistical design pattern; COVARIANCE DETERMINANT ESTIMATOR; PRINCIPAL COMPONENT ANALYSIS; PROJECTION-PURSUIT APPROACH; MULTIVARIATE LOCATION; DISPERSION MATRICES; OUTLIER DETECTION; FAST ALGORITHM; S-ESTIMATORS; EFFICIENCY; BEHAVIOR;
D O I：
10.1016/j.ins.2012.10.017
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Data outliers or other data inhomogeneities lead to a violation of the assumptions of traditional statistical estimators and methods. Robust statistics offers tools that can reliably work with contaminated data. Here, outlier detection methods in low and high dimension, as well as important robust estimators and methods for multivariate data are reviewed, and the most important references to the corresponding literature are provided. Algorithms are discussed, and routines in R are provided, allowing for a straightforward application of the robust methods to real data. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码：4 / 20
页数：17
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