Principal component analysis (PCA) is a multivariate technique that analyzes a data table in
which observations are described by several inter‐correlated quantitative dependent …

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 White matter in the human brain serves a critical role in organizing
distributed neural networks. Diffusion magnetic resonance imaging (dMRI) has enabled the …

The theory and practice of statistical methods in situations where the available data are
functions (instead of real numbers or vectors) is often referred to as Functional Data Analysis …

From sparse to dense functional data and beyond Page 1 The Annals of Statistics 2016, Vol.
44, No. 5, 2281–2321 DOI: 10.1214/16-AOS1446 © Institute of Mathematical Statistics, 2016 …

Functional data analysis has been a popular statistical research topic for the past three
decades. Functional data are often obtained by observing a number of subjects over time …

We consider nonparametric estimation of the mean and covariance functions for
functional/longitudinal data. Strong uniform convergence rates are developed for estimators …

The Wasserstein metric, Wasserstein–Fréchet mean, simulation results and additional
proofs. The supplementary material includes additional discussion on the Wasserstein …

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 …

We propose an extended version of the classical Karhunen-Loève expansion of a
multivariate random process, termed a normalized multivariate functional principal …