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dc.contributor.authorBiancolillo, Alessandra
dc.contributor.authorLiland, Kristian Hovde
dc.contributor.authorMåge, Ingrid
dc.contributor.authorNæs, Tormod
dc.contributor.authorBro, Rasmus
dc.identifier.citationChemometrics and Intelligent Laboratory Systems 2016, 156:89-101
dc.description.abstractThe focus of the present paper is to propose and discuss different procedures for performing variable selection in a multi-block regression context. In particular, the focus is on two multi-block regression methods: Multi-Block Partial Least Squares (MB-PLS) and Sequential and Orthogonalized Partial Least Squares (SO-PLS) regression. A small simulation study for regular PLS regression was conducted in order to select the most promising methods to investigate further in the multi-block context. The combinations of three variable selection methods with MB-PLS and SO-PLS are examined in detail. These methods are Variable Importance in Projection (VIP) Selectivity Ratio (SR) and forward selection. In this paper we focus on both prediction ability and interpretation. The different approaches are tested on three types of data: one sensory data set, one spectroscopic (Raman) data set and a number of simulated multi-block data sets.
dc.rightsNavngivelse 3.0 Norge*
dc.titleVariable selection in multi-block regression
dc.typeJournal article
dc.relation.projectNorges forskningsråd: 225096
dc.relation.projectNofima AS: 201302
dc.relation.projectNorges forskningsråd: 225347
dc.relation.projectNorges forskningsråd: 225062
dc.relation.projectNofima AS: 201309
dc.relation.projectNofima AS: 201308

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Navngivelse 3.0 Norge
Except where otherwise noted, this item's license is described as Navngivelse 3.0 Norge