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[BCS18] Prediction for regularized clusterwise multiblock regression
Revue Internationale avec comité de lecture : Journal Applied Stochastic Models in Business and Industry, vol. 34(6), pp. 852-867, 2018, (doi:10.1002/asmb.2335)Mots clés: Clusterwise regression, multiblock component method, dimension reduction, multicollinearity, multiblock, classification
Résumé:
In a large variety of fields such as epidemiology, process monitoring, chemometrics,
marketing and social sciences among others, many research questions pertain to regression
analysis from large datasets. Although in some cases standard regression will suffice, modelling is sometimes more challenging for various reasons: (i) explain several
variables, (ii) with a large number of explanatory variables organized into meaningful—
usually ill-conditioned—multidimensional matrices, (iii) where observations come from
different sub-populations and (iv) with the opportunity to predict new observations. Although
some developed methods partially meet these challenges, none of them covers all
these aspects. To fill this gap, a new method—called regularized clusterwise multiblock
regression (CW.rMBREG)—is proposed. The method CW.rMBREG combines clustering
and a component-based (multiblock) model associated with a well-defined criterion to
optimize. It provides simultaneously a partition of the observations into clusters along
with the regression coe_cients associated with each cluster. To go further, we propose
to investigate a key feature generally neglected in clusterwise regression: the prediction
of new observations. The usefulness of CW.rMBREG is illustrated on the basis of both
a simulation study and a real example in the field of indoor air quality. It results that
CW.rMBREG improves the quality of the prediction and facilitates the interpretation of
complex ill-conditioned data. The proposed method is available for users though the R
package mbclusterwise.
Collaboration:
ANSES
BibTeX
| @article { | |||
| BCS18, | |||
| title | = | "{Prediction for regularized clusterwise multiblock regression}", | |
| author | = | "S. Bougeard and V. Cariou and G. Saporta and N. Niang Keita", | |
| journal | = | "Applied Stochastic Models in Business and Industry", | |
| year | = | 2018, | |
| volume | = | 34, | |
| number | = | 6, | |
| pages | = | "852-867", | |
| doi | = | "10.1002/asmb.2335", | |
| } | |||
