Functional data analysis (FDA) involves the analysis of data whose ideal units of
observation are functions defined on some continuous domain, and the observed data …

Advances in data collection and storage have tremendously increased the presence of
functional data, whose graphical representations are curves, images or shapes. As a new …

Recent years have seen an explosion of activity in the field of functional data analysis (FDA),
in which curves, spectra, images and so on are considered as basic functional data units. A …

The selection of grouped variables using the random forest algorithm is considered. First a
new importance measure adapted for groups of variables is proposed. Theoretical insights …

We describe and analyse a longitudinal diffusion tensor imaging study relating changes in
the microstructure of intracranial white matter tracts to cognitive disability in multiple …

Sugarcane bagasse ash (SCBA) is produced during the cogeneration of dry sugarcane
bagasse in the sugar and ethanol industries. Managing the huge amount of SCBA is a …

Modern research data, where a large number of functional predictors is collected on few
subjects are becoming increasingly common. In this paper we propose a variable selection …

The increasing richness in the volume and types of data in the financial domain provides
unprecedented opportunities for understanding the stock market more comprehensively and …

Functional data analysis is a field of growing importance in Statistics. In particular, the
functional linear model with scalar response is surely the model that has attracted more …

Functional data analysis (FDA) is a statistical framework that allows for the analysis of
curves, images, or functions on higher dimensional domains. The goals of FDA, such as …