We consider estimation of mean and covariance functions of functional snippets, which are
short segments of functions possibly observed irregularly on an individual specific …

Partially observed functional data are frequently encountered in applications and are the
object of an increasing interest by the literature. We here address the problem of measuring …

We consider classification of functional data into two groups by linear classifiers based on
one-dimensional projections of functions. We reformulate the task of finding the best …

Environmental signals, acquired, eg, by remote sensing, often present large gaps of missing
observations in space and time. In this work, we present an innovative approach to identify …

Estimation of mean and covariance functions is fundamental for functional data analysis.
While this topic has been studied extensively in the literature, a key assumption is that there …

Ensuring the long-term sustainability of food systems and the welfare of current and future
generations depends critically on the economic and environmental sustainability of …

In functional data analysis it is usually assumed that all functions are completely, densely or
sparsely observed on the same domain. Recent applications have brought attention to …

Accounting for phase variability is a critical challenge in functional data analysis. To
separate it from amplitude variation, functional data are registered, ie, their observed …

When functional data are observed on parts of the domain, it is of interest to recover the
missing parts of curves. Kraus (2015) proposed a linear reconstruction method based on …

A novel framework for sparse functional clustering that also embeds an alignment step is
here proposed. Sparse functional clustering entails estimating the parts of the curves' …