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Published in:
2004 | OriginalPaper | Chapter
Partial Mixture Estimation and Outlier Detection in Data and Regression
Author : D. W. Scott
Published in: Theory and Applications of Recent Robust Methods
Publisher: Birkhäuser Basel
Included in: Professional Book Archive
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The covariance matrix is a key component of many multivariate robust procedures, whether or not the data are assumed to be Gaussian. We examine the idea of robustly fitting a mixture of multivariate Gaussian densities in the situation when the number of components estimated is intentionally too few. Using a minimum distance criterion, we show how useful results may be obtained in practice. Application areas are numerous, and examples will be provided.