[NBS15a] Clusterwise multiblock PLS regression

Conférence Internationale avec comité de lecture : CFE-CMStatistics 2015, December 2015, pp.195, Londres, Grande Bretagne,

Auteurs: N. Niang Keita , S. Bougeard , G. Saporta

Mots clés: clusterwise regression, multiblock, PLS regressio

Résumé: Clusterwise regression methods aim at partitioning data sets into clusters characterized by their specific coefficients in the regression model. Usually, one dependent variable is linearly related to independent variables which are in a single data table. We present clusterwise multiblock PLS: an extension of clusterwise PLS regression to multiresponse variables and independent variables organized in meaningful blocks. This block structure is taken into account through a set of weights based on the importance of the block on the response prediction. This new method provides a partition of the data such as each of its cluster is associated with its own PLS model, which is then used to improve the overall fit of the prediction step. To do so, a new observation is first assigned to the relevant cluster minimizing a specific distance measure or maximizing the class membership probability. The prediction is then performed using the associated local model or using model averaging strategies. This general approach is based on a clear criterion to minimize and can be directly extended to other multiblock regression methods. The clusterwise multiblock PLS regression will be illustrated on both synthetic and real data.

Commentaires: 8th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2015) ISBN 978-9963-2227-0-4

Equipe: msdma

Collaboration: ANSES

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BibTeX

@inproceedings {
	NBS15a,
	title	=	"{Clusterwise multiblock PLS regression}",
	author	=	" N. Niang Keita and S. Bougeard and G. Saporta ",
	booktitle	=	"{CFE-CMStatistics 2015}",
	year	=	2015,
	month	=	"December",
	pages	=	"195",
	address	=	"Londres, Grande Bretagne",
	note	=	"{8th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (CMStatistics 2015) ISBN 978-9963-2227-0-4}",
}