High-Breakdown Robust Multivariate Methods

Details High-Breakdown Robust Multivariate Methods High-Breakdown Robust Multivariate Methods

2008-08-05

arxiv.org/abs/0808.0657v1

Statistical Science 2008, Vol. 23, No. 1, 92-119

Statistics

Methodology

Published in at http://dx.doi.org/10.1214/088342307000000087 the Statistical Science (http://www.imstat.org/sts/) by the Insti

Scientific paper

10.1214/088342307000000087

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.

Affiliated with

Also associated with

No associations

Rating

High-Breakdown Robust Multivariate Methods does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with High-Breakdown Robust Multivariate Methods, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and High-Breakdown Robust Multivariate Methods will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-550340