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 …

Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear
Operators provides a uniquely broad compendium of the key mathematical concepts and …

Random forests are a powerful method for nonparametric regression, but are limited in their
ability to fit smooth signals. Taking the perspective of random forests as an adaptive kernel …

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 …

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

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

Classical time series models have serious difficulties in modeling and forecasting the
enormous fluctuations of electricity spot prices. Markov regime switch models belong to the …

A polynomial spline estimator is proposed for the mean function of dense functional data
together with a simultaneous confidence band which is asymptotically correct. In addition …