A PLS KERNEL ALGORITHM FOR DATA SETS WITH MANY VARIABLES AND FEW OBJECTS .2. CROSS-VALIDATION, MISSING DATA AND EXAMPLES

This is Part II of a series concerning the PLS kernel algorithm for da ta sets with many variables and few objects. Here the issues of cross- validation and missing data are investigated. Both partial and full cr oss-validation are evaluated in terms of predictive residuals and spee d and are illustrated on real examples. Two related approaches to the solution of the missing data problem are presented. One is a full EM a lgorithm and the second a reduced EM algorithm which applies when the number of missing values is small. The two examples are multivariate c alibration data sets. The first set consists of UV-visible data measur ed on mixtures of four metal ions. The second example consists of FT-I R measurements on mixtures consisting of four different organic substa nces.