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 …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

In modern experiments, functional and nonfunctional data are often encountered
simultaneously when observations are sampled from random processes and high …

The use of imaging markers to predict clinical outcomes can have a great impact in public
health. The aim of this article is to develop a class of generalized scalar-on-image …

Functional principal component analysis (FPCA) has become the most widely used
dimension reduction tool for functional data analysis. We consider functional data measured …

Functional data analysis (FDA) is a fast-growing area of research and development in
statistics. While most FDA literature imposes the classical L 2 Hilbert structure on function …

Although deep learning has achieved remarkable success in various scientific machine
learning applications, its opaque nature poses concerns regarding interpretability and …

We investigate a class of partially linear functional additive models (PLFAM) that predicts a
scalar response by both parametric effects of a multivariate predictor and nonparametric …

We propose a multistage testing procedure to determine the order p of a functional
autoregressive process, FAR (p). At its core is the representation of the FAR (p) process as a …

We consider a functional linear Cox regression model for characterizing the association
between time‐to‐event data and a set of functional and scalar predictors. The functional …