PLS Approach for clusterwise linear regression on functional data
Preda, Cristian ; Saporta, Gilbert
HAL, hal-01124925 / Harvested from HAL
Text on HAL
Partial Least Squares approach is used for the clusterwise linear regression algorithm when the set of predictor variables forms a L2 continuous stochastic process.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.
Publié le : 2004-01-01
Classification:  [INFO]Computer Science [cs],  [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] 
@article{hal-01124925,
     author = {Preda, Cristian and Saporta, Gilbert},
     title = {PLS Approach for clusterwise linear regression on functional data},
     journal = {HAL},
     volume = {2004},
     number = {0},
     year = {2004},
     language = {en},
     url = {http://dml.mathdoc.fr/item/hal-01124925}
}

Preda, Cristian; Saporta, Gilbert. PLS Approach for clusterwise linear regression on functional data. HAL, Tome 2004 (2004) no. 0, . http://gdmltest.u-ga.fr/item/hal-01124925/