Independent component analysis (ICA) is a data analysis tool that can be seen as a
refinement of principal component analysis or factor analysis. ICA recovers the structures in …

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 …

A general method for exploring multivariate data by comparing different estimates of
multivariate scatter is presented. The method is based on the eigenvalue–eigenvector …

During a clinical trial of a new treatment, a large number of variables are measured to
monitor the safety of the treatment. It is important to detect outlying observations which may …

The present paper examines proportional goodness of fit to variables recorded on
individuals, the variances and covariances of the variables, and the form and distances …

It is fair to say that most, if not all, VANET applications rely on accurate location information. It
is therefore imperative to provide mechanisms that ensure the integrity, availability, and …

Abstract Recently, Spurek, Tabor, Struksi and Smieja (2018) suggested an independent
component analysis method that is obtained via a generalized eigenvalue decomposition …

Scatter matrices generalize the covariance matrix and are useful in many multivariate data
analysis methods, including well-known principal component analysis (PCA), which is …

The purpose of this paper is to describe the R package {PTAk and how the spatio-temporal
context can be taken into account in the analyses. Essentially PTAk () is a multiway …

Le biplot est un outil graphique pour visualiser des données arrangées en forme de matrice
(Gabriel, 1971, 1982; Gower et Hand, 1996). Les graphiques ont des usages divers en …