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 …

Gathering knowledge of supply curves in electricity markets is critical to both energy
producers and regulators. Indeed, power producers strategically plan their generation of …

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 …

Compositional data have posed challenges to current classification methods owing to the
non-negative and unit-sum constraints, especially when a certain of the components are …

Abstract Electronic Health Record (EHR) data are captured over time as patients receive
care. Accordingly, variations among patients, such as when a patient presents for care …

Here, we intend to introduce an efficient, robust curve alignment algorithm with respect to the
group of special affine transformations of the plane denoted by SA (2, R). Such a group of …

Accounting for phase variability is a critical challenge in functional data analysis. To
separate it from amplitude variation, functional data are registered, ie, their observed …

In this work, we develop a new set of Bayesian models to perform registration of real-valued
functions. A Gaussian process prior is assigned to the parameter space of time warping …

We consider separating and joint modelling amplitude and phase variations for functional
data in an identifiable manner. To rigorously address this separability issue, we introduce …

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