Principal component analysis (PCA) is often applied for analyzing data in the most diverse
areas. This work reports, in an accessible and integrated manner, several theoretical and …

The viewpoint taken in much of this text is that PCA is mainly a descriptive tool with no need
for rigorous distributional or model assumptions. This implies that it can be used on a wide …

Many biodemographic studies use biomarkers of inflammation to understand or predict
chronic disease and aging. Inflamm-aging, ie chronic low-grade inflammation during aging …

The subject of allometry is variation in morphometric variables or other features of organisms
associated with variation in size. Such variation can be produced by several biological …

During the early steps of the construction of composite health measures, principal
component analysis (PCA) is commonly used to identify 'latent'factors that underlie observed …

New insights are given into why the problem of detecting multivariate outliers can be difficult
and why the difficulty increases with the dimension of the data. Significant improvements in …

L'analyse des données textuelles (ADT) permet d'explorer et de visualiser les recueils de
textes les plus divers: œuvres littéraires, transcriptions d'entretien, discours politiques …

A few repeats of a simple forward search from a random starting point are shown to provide
sufficiently robust parameter estimates to reveal masked multiple outliers. The stability of the …

Static, ontogenetic, and evolutionary allometry in all five larval instars of nine species of the
water strider genera Gerris and Aquarius were compared using a multivariate approach …

Mahalanobis-type distances in which the shape matrix is derived from a consistent, high-
breakdown robust multivariate location and scale estimator have an asymptotic chi-squared …