.png){kind=icon}
Return
Robust statistics for outlier detection
.png){kind=icon}
PRE
AI
DOI：10.1002/widm.2.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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
Keywords:
PRINCIPAL COMPONENT ANALYSIS
PROJECTION-PURSUIT APPROACH
ESTIMATORS
LOCATION
COVARIANCE
REGRESSION
DISPERSION
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.7
Papers:
548
Citations:
5.3K
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSavepLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC.png)
Gene
GeneIF0
PREAI
ShareSave
ShareSaveShareSave
View more