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 …

With the advance of modern technology, more and more data are being recorded
continuously during a time interval or intermittently at several discrete time points. These are …

Introduction to Functional Data Analysis provides a concise textbook introduction to the field.
It explains how to analyze functional data, both at exploratory and inferential levels. It also …

In the last two decades, the growth of computational resources has made it possible to
handle generalized additive models (GAMs) that formerly were too costly for serious …

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 …

Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially
non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM …

We propose an extensive framework for additive regression models for correlated functional
responses, allowing for multiple partially nested or crossed functional random effects with …

Researchers are increasingly interested in regression models for functional data. This article
discusses a comprehensive framework for additive (mixed) models for functional responses …

P-splines first appeared in the limelight twenty years ago. Since then they have become
popular in applications and in theoretical work. The combination of a rich B-spline basis and …

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 …