Functional data analysis (FDA) is a statistical framework that allows for the analysis of
curves, images, or functions on higher dimensional domains. The goals of FDA, such as …

In many applications, smooth processes generate data that are recorded under a variety of
observational regimes, including dense sampling and sparse or fragmented observations …

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research
through new terminology for discussing the often many possible approaches to the analysis …

We provide statistical analysis methods for samples of curves in two or more dimensions,
where the image, but not the parameterization of the curves, is of interest and suitable …

Time war** function provides a mathematical representation to measure phase variability
in functional data. Recent studies have developed various approaches to estimate optimal …

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze
group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This …

In this work, we introduce a multidimensional, multiple-output Gaussian process (GP) model
for statistical shape analysis, specifically addressing the intricate task of characterizing …

In the shape analysis approach to computer vision problems, one treats shapes as points in
an infinite-dimensional Riemannian manifold, thereby facilitating algorithms for statistical …

The time-lag effect between temperature and thermal displacement may induce the
displacement-based safety assessment results of long-span bridges to derivate from the …

In the field of bridge health monitoring, it is ubiquitous to model the relationship for
monitoring data. However, in many cases, especially for concrete bridge structures, field …