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 …

The book describes the use of smoothing techniques in statistics, including both density
estimation and nonparametric regression. Considerable advances in research in this area …

We propose a nonparametric method to perform functional principal components analysis
for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal …

With contributions from some of the most prominent researchers in the field, this carefully
edited collection provides a clear, comprehensive, and unified overview of recent …

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 …

Although standard mixed effects models are useful in a range of studies, other approaches
must often be used in correlation with them when studying complex or incomplete data …

The elements of a multivariate dataset are often curves rather than single points. Functional
principal components can be used to describe the modes of variation of such curves. If one …

The use of principal component methods to analyze functional data is appropriate in a wide
range of different settings. In studies of “functional data analysis,” it has often been assumed …

We present a technique for extending generalized linear models to the situation where some
of the predictor variables are observations from a curve or function. The technique is …