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 …

Developed by the authors, generalized structured component analysis is an alternative to
two longstanding approaches to structural equation modeling: covariance structure analysis …

We develop fast fitting methods for generalized functional linear models. The functional
predictor is projected onto a large number of smooth eigenvectors and the coefficient …

We introduce models for the analysis of functional data observed at multiple time points. The
model can be viewed as the functional analog of the classical mixed effects model where …

Marketing resource allocation has been a topic of intense scrutiny, yet the literature on the
topic has not paid adequate attention to the fact that the effectiveness of marketing-mix …

A general framework for smooth regression of a functional response on one or multiple
functional predictors is proposed. Using the mixed model representation of penalized …

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 …

We propose two fast covariance smoothing methods and associated software that scale up
linearly with the number of observations per function. Most available methods and software …