Large datasets are increasingly common and are often difficult to interpret. Principal
component analysis (PCA) is a technique for reducing the dimensionality of such datasets …

Functional data analysis (FDA), which is a branch of statistics on modeling infinite
dimensional random vectors resided in functional spaces, has become a major research …

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 …

We introduce methods and theory for functional or curve time series with long-range
dependence. The temporal sum of the curve process is shown to be asymptotically normally …

Functional principal component analysis (FPCA) is an extension of conventional principal
component analysis (PCA) that allows the processing of functional data. Besides the …

Motivated by recent work involving the analysis of biomedical imaging data, we present a
novel procedure for constructing simultaneous confidence corridors for the mean of imaging …

Covariance estimation is essential yet underdeveloped for analysing multivariate functional
data. We propose a fast covariance estimation method for multivariate sparse functional data …

Graphical models express conditional independence relationships among variables.
Although methods for vector-valued data are well established, functional data graphical …

Variable selection is considered in the setting of supervised binary classification with
functional data X (t), t∈ 0, 1. By" variable selection" we mean any dimension-reduction …

When classification methods are applied to high-dimensional data, selecting a subset of the
predictors may lead to an improvement in the predictive ability of the estimated model, in …