Clusterwise PLS regression on a stochastic process

Cristian Preda (1) , Gilbert Saporta (2)

1 Université de Lille
2 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage

The clusterwise linear regression is studied when the set of predictor variables forms a L2-continuous stochastic process. For each cluster the estimators of the regression coefficients are given by partial least square regression. The number of clusters is treated as unknown and the convergence of the clusterwise algorithm is discussed. The approach is compared with other methods via an application on stock-exchange data.

Domaines: Statistiques [math.ST]; Informatique [cs]

Cristian Preda, Gilbert Saporta. Clusterwise PLS regression on a stochastic process. Computational Statistics and Data Analysis, 2005, 49, pp.99-108. ⟨10.1016/j.csda.2004.05.002⟩. ⟨hal-01124969⟩