Predictive-property-ranked variable reduction with final complexity adapted models in partial least squares modeling for multiple responses.

For partial least-squares regression with one response (PLS1), many variable-reduction methods have been developed. However, only a few address the case of multiple-response partial-least-squares (PLS2) modeling. The calibration performance of PLS1 can be improved by elimination of uninformative variables.

Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking.

The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative variables. Many variable-reduction methods are based on so-called predictor-variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the variable-reduction process. Recently, a new predictive-property-ranked variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced.

Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property.

[Frail elderly. Identification of a population at risk].

In the future the number of frail independently living older people will continue to increase. It is unclear however, which people are meant exactly by those frail elderly. The aim of this article is to discuss the concept of frailty and its adequacy in identifying the frail elderly population.