Abstract
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.
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Scott, D.W. (2004). Partial Mixture Estimation and Outlier Detection in Data and Regression. In: Hubert, M., Pison, G., Struyf, A., Van Aelst, S. (eds) Theory and Applications of Recent Robust Methods. Statistics for Industry and Technology. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-7958-3_26
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DOI: https://doi.org/10.1007/978-3-0348-7958-3_26
Publisher Name: Birkhäuser, Basel
Print ISBN: 978-3-0348-9636-8
Online ISBN: 978-3-0348-7958-3
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