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Sparse common and distinctive covariates regression

Park,Soogeun
; 
Ceulemans,Eva
; 
Van Deun,Katrijn
Abstract
Having large sets of predictors from multiple sources concerning the same observation units and the same criterion is becoming increasingly common in chemometrics. When analyzing such data, chemometricians often have multiple objectives: prediction of the criterion, variable selection, and identification of underlying processes associated to individual predictor sources or to several sources jointly. Existing methods offer solutions regarding the first two aims of uncovering the predictive mechanisms and relevant variables therein for a single block of predictor variables, but the challenge of uncovering joint and distinctive predictive mechanisms and the relevant variables therein in the multisource setting still needs to be addressed. To this end, we present a multiblock extension of principal covariates regression that aims to find the complex mechanisms in which several or single sources may be involved; taken together, these mechanisms predict an outcome of interest. We call this method sparse common and distinctive covariates regression (SCD‐CovR). Through a simulation study, we demonstrate that SCD‐CovR provides competitive solutions when compared with related methods. The method is also illustrated via an application to a publicly available dataset.
Description
This research was funded by a personal grant from the Netherlands Organisation for Scientific Research [NWO‐VIDI 452.16.012] awarded to Katrijn Van Deun. The funder did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank the anonymous reviewers for providing their valuable comments and suggestions on improving the manuscript.
Date
2021
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Tilburg University Research Output Collection
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COMPONENTS-ANALYSIS, INFORMATION, JOINT, PREDICTION, VARIABLE SELECTION, common and distinctive processes, data integration, multiblock data, principal covariates regression, variable selection
Citation
Park, S, Ceulemans, E & Van Deun, K 2021, 'Sparse common and distinctive covariates regression', Journal of Chemometrics, vol. 35, no. 2, e3270. https://doi.org/10.1002/cem.3270
URI
https://hdl.handle.net/20.500.14602/73604
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info:eu-repo/semantics/openAccess
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