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PLS regression on a stochastic process

Cristian Preda 1 Gilbert Saporta 2
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Partial least squares (PLS) regression on an L2-continuous stochastic process is an extension of the finite set case of predictor variables. The PLS components existence as eigenvectors of some operator and convergence properties of the PLS approximation are proved. The results of an application to stock-exchange data will be compared with those obtained by other methods.
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https://hal.archives-ouvertes.fr/hal-01124945
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Submitted on : Friday, March 6, 2015 - 10:53:26 AM
Last modification on : Monday, March 16, 2020 - 1:16:14 AM

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Cristian Preda, Gilbert Saporta. PLS regression on a stochastic process. Computational Statistics and Data Analysis, Elsevier, 2005, 48, pp.149-158. ⟨10.1016/j.csda.2003.10.003⟩. ⟨hal-01124945⟩

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