Abstract
Multivariate calibration uses an estimated relationship between a multivariate response Y and an explanatory vector X to predict unknown X in future from further observed responses. Up to now very little has been written about robust calibration. An approach can be based on the outliers deletion methods. An alternative is to employ robust procedures. The purpose of this paper is to present multivariate calibration methods which are able to detect and investigate those observations which differ from the bulk of the data or to identify subgroups of observations. Particular attention will be paid to the forward search approach.
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Salini, S. (2006). Robust Multivariate Calibration. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_25
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DOI: https://doi.org/10.1007/3-540-35978-8_25
Publisher Name: Springer, Berlin, Heidelberg
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