Validation in principal components analysis applied to EEG data.

The well-known multivariate technique Principal Components Analysis (PCA) is usually applied to a sample, and so component scores are subjected to sampling variability. However, few studies address their stability, an important topic when the sample size is small. This work presents three validation procedures applied to PCA, based on confidence regions generated by a variant of a nonparametric bootstrap called the partial bootstrap: (i) the assessment of PC scores variability by the spread and overlapping of "confidence regions" plotted around these scores; (ii) the use of the confidence regions centroids as a validation set; and (iii) the definition of the number of nontrivial axes to be retained for analysis.

Multiple correspondence analysis in predictive logistic modelling: application to a living-donor kidney transplantation data.

This work deals with the use of multiple correspondence analysis (MCA) and a weighted Euclidean distance (the tolerance distance) as an exploratory tool in developing predictive logistic models. The method was applied to a living-donor kidney transplant data set with 109 cases and 13 predictors. This approach, followed by backward and forward selection procedures, yielded two models, one with four and another with two predictors.

A heuristic index for selecting similar categories in multiple correspondence analysis applied to living donor kidney transplantation.

This work introduces a heuristic index (the "tolerance distance") to define the "closeness" of two variable categories in multiple correspondence analysis (MCA). This index is a weighted Euclidean distance where weightings are based on the "importance" of each MCA axis, and variable categories were considered to be associated when their distances were below the tolerance distance. This approach was applied to a renal transplantation data.