Robust multivariate methods in Chemometrics
| Autor: | Filzmoser, Peter, Serneels, Sven, Maronna, Ricardo, Croux, Christophe |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | in: Comprehensive Chemometrics, 2nd Edition, Steven Brown, Roma Tauler and Beata Walczak (Eds.), Elsevier, 26 May 2020, ISBN: 9780444641656, Volume 3, Section 3.19, pages 393-430 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/B978-0-12-409547-2.14642-6 |
| Popis: | This chapter presents an introduction to robust statistics with applications of a chemometric nature. Following a description of the basic ideas and concepts behind robust statistics, including how robust estimators can be conceived, the chapter builds up to the construction (and use) of robust alternatives for some methods for multivariate analysis frequently used in chemometrics, such as principal component analysis and partial least squares. The chapter then provides an insight into how these robust methods can be used or extended to classification. To conclude, the issue of validation of the results is being addressed: it is shown how uncertainty statements associated with robust estimates, can be obtained. Comment: This article is an update of: P. Filzmoser, S. Serneels, R. Maronna, P.J. Van Espen, 3.24 - Robust Multivariate Methods in Chemometrics, in Comprehensive Chemometrics, 1st Edition, edited by Steven D. Brown, Rom\'a Tauler, Beata Walczak, Elsevier, 2009, https://doi.org/10.1016/B978-044452701-1.00113-7 |
| Databáze: | arXiv |
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