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Bühlmann, Peter

Partial least squares for heterogeneous data. (English) Zbl 1366.62131

Abdi, Hervé (ed.) et al., The multiple facets of partial least squares methods. PLS, Paris, France, May 26–28, 2014. Cham: Springer (ISBN 978-3-319-40641-1/hbk; 978-3-319-40643-5/ebook). Springer Proceedings in Mathematics & Statistics 173, 3-15 (2016).
Summary: Large-scale data, where the sample size and the dimension are high, often exhibits heterogeneity. This can arise for example in the form of unknown subgroups or clusters, batch effects or contaminated samples. Ignoring these issues would often lead to poor prediction and estimation. We advocate the maximin effects framework [N. Meinshausen and the author, Ann. Stat. 43, No. 4, 1801–1830 (2015; Zbl 1317.62059)] to address the problem of heterogeneous data. In combination with partial least squares (PLS) regression, we obtain a new PLS procedure which is robust and tailored for large-scale heterogeneous data. A small empirical study complements our exposition of new PLS methodology.
For the entire collection see [Zbl 1356.62003].

Cited in 1 Document

MSC:

62J05 Linear regression; mixed models
62C20 Minimax procedures in statistical decision theory

Keywords:

partial least square regression (PLSR); heterogeneous data; big data; minimax; maximin

Citations:

Zbl 1317.62059

Software:

MEMSS; ElemStatLearn; R; S-PLUS
Cite Review PDF
Full Text: DOI

References:

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