The paper “Variable selection in multi-block regression” authored by Alessandra Biancolillo, Kristian Hovde Liland, Ingrid Måge, Tormod Næs and Rasmus Bro has been published in Chemometrics and Intelligent Laboratory Systems.
The 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 MBPLS 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.