Robust statistics for outlier detection

doi:10.1002/widm.2

2011-01-06

565

PRE

Peter J. Rousseeuw *

Mia Hubert

DOI：10.1002/widm.2

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摘要

En 中文

When analyzing data, outlying observations cause problems because they may strongly influence the result. Robust statistics aims at detecting the outliers by searching for the model fitted by the majority of the data. We present an overview of several robust methods and outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data such as estimation of location and scatter, linear regression, principal component analysis, and classification. (C) 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 73-79 DOI: 10.1002/widm.2

Keyword:

PRINCIPAL COMPONENT ANALYSIS

PROJECTION-PURSUIT APPROACH

ESTIMATORS

LOCATION

COVARIANCE

REGRESSION

DISPERSION

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期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

IF:

11.7

论文数:

542

被引数:

5.3K

机构

KU Leuven

学者数:

5.7W

论文数: 5.2W

被引数: 8.1W

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