High-breakdown robust multivariate methods

doi:10.1214/088342307000000087

High-breakdown robust multivariate methods

2008-02-01

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DOI：10.1214/088342307000000087

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When applying a statistical method in practice it often occurs that some observations deviate from the usual assumptions. However, many classical methods are sensitive to outliers. The goal of robust statistics is to develop methods that are robust against the possibility that one or several unannounced outliers may occur anywhere in the data. These methods then allow to detect outlying observations by their residuals from a robust fit. We focus on high-breakdown methods, which can deal with a substantial fraction of outliers in the data. We give an overview of recent high-breakdown robust methods for multivariate settings such as covariance estimation, multiple and multivariate regression, discriminant analysis, principal components and multivariate calibration.

Keywords:

breakdown value

influence function

multivariate statistics

outliers

partial least squares

principal components

regression

robustness

High-breakdown robust multivariate methods

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