The correspondence analysis family tree is ever growing. From its roots that lie in Europe (or
the United Kingdom, depending on which perspective one takes), it has matured more …

Existing approaches for multivariate functional principal component analysis are restricted to
data on the same one-dimensional interval. The presented approach focuses on multivariate …

Clustering techniques for functional data are reviewed. Four groups of clustering algorithms
for functional data are proposed. The first group consists of methods working directly on the …

The first model-based clustering algorithm for multivariate functional data is proposed. After
introducing multivariate functional principal components analysis (MFPCA), a parametric …

The clusterwise linear regression is studied when the set of predictor variables forms a L2-
continuous stochastic process. For each cluster the estimators of the regression coefficients …

Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA)
when predictors are data of functional type (curves). Based on the equivalence between …

A new method for clustering functional data is proposed under the name Funclust. This
method relies on the approximation of the notion of probability density for functional random …

With the emergence of numerical sensors in many aspects of everyday life, there is an
increasing need in analyzing multivariate functional data. This work focuses on the …

Recently, an R package was developed for categorical functional data analysis (CFDA). This
statistical approach extends the usual functional data analysis to temporal categorical data …

Data in the form of a continuous vector function on a given interval are referred to as
multivariate functional data. These data are treated as realizations of multivariate random …