Name
Abstract | ||
|---|---|---|
Several papers have already stressed the interest of latent root regression and its similarities to partial least squares regression. A new formulation of this method which makes it even simpler than the original method to set up a prediction model is discussed. Furthermore, it is shown how this method can be extended not only to the case where it is desired to predict several response variables from a set of predictors but also to the multiblock setting where the aim is to predict one or several data sets from several other data sets. The interest of the method is illustrated on the basis of a data set pertaining to epidemiology. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s00180-007-0036-1 | Computational Statistics |
Keywords | DocType | Volume |
latent root regression · multiblock analysis · latent variables · partial least square regression,Multiblock latent root regression,squares regression,new formulation,original method,data set,latent root regression,response variable,prediction model | Journal | 22 |
Issue | ISSN | Citations |
2 | 1613-9658 | 1 |
PageRank | References | Authors |
0.41 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stéphanie Bougeard | 1 | 11 | 3.10 |
| Mohamed Hanafi | 2 | 12 | 4.12 |
| Mostafa El Qannari | 3 | 16 | 4.40 |