Abstract
In the multivariate one-sample location model, only L-estimators are affine equivariant; we propose a class of flexible robust, affine equivariant L-estimators of location, for distributions invoking affine-invariance of Mahalanobis distances of individual observations. An involved iteration process for their computation is numerically illustrated.
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Acknowledgements
We highly appreciate the long-term cooperation with Hannu Oja and his group, and hope in new fresh interesting joint ideas in the future. We also thank the Editors for the organization of this Volume.
The authors thank two Referees for their comments, which helped to better understanding the text. P. K. Sen gratefully acknowledges the support of the Cary C. Boshamer Research Foundation at UNC. Research of J. Jurečková and of J. Picek was supported by the Czech Republic Grant 15–00243S.
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Sen, P.K., Jurečková, J., Picek, J. (2015). Affine Equivariant Rank-Weighted L-Estimation of Multivariate Location. In: Nordhausen, K., Taskinen, S. (eds) Modern Nonparametric, Robust and Multivariate Methods. Springer, Cham. https://doi.org/10.1007/978-3-319-22404-6_18
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DOI: https://doi.org/10.1007/978-3-319-22404-6_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22403-9
Online ISBN: 978-3-319-22404-6
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