The theory and practice of statistical methods in situations where the available data are
functions (instead of real numbers or vectors) is often referred to as Functional Data Analysis …

Deviations from theoretical assumptions together with the presence of certain amount of
outlying observations are common in many practical statistical applications. This is also the …

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 …

Energy suppliers are facing ever increasing competition, so that factors like quality and
continuity of offered services must be properly taken into account. Furthermore, in the last …

This paper analyzes outlier detection for functional data by means of functional depths,
which measures the centrality of a given curve within a group of trajectories providing center …

The problem of curve clustering when curves are misaligned is considered. A novel
algorithm is described, which jointly clusters and aligns curves. The proposed procedure …

Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …

We present two strategies for detecting patterns and clusters in high-dimensional time-
dependent functional data. The use on wavelet-based similarity measures, since wavelets …

A full understanding of emotions and emotion characteristics can only be reached when
their dynamic nature is taken into account. As such, a primary objective of the present study …

Functional data analysis is an active field of research in Statistics. This Special Issue on
Statistics for Functional Data contains a selected set of contributions which covers a scope …