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 …

Functional data analysis (FDA) studies data that include infinite-dimensional functions or
objects, generalizing traditional univariate or multivariate observations from each study unit …

Emerging technologies generate data sets of increased size and complexity that require
new or updated statistical inferential methods and scalable, reproducible software. These …

In this study, we conducted an analysis of a multifrequency acoustics dataset acquired from
scientific echosounders in the West African water. Our objective was to explore the spatial …

We develop a theory for functional independent component analysis in an infinite-
dimensional framework using Sobolev spaces that accommodate smoother functions. The …

This paper introduces a model for longitudinal functional data analysis that accounts for
pointwise skewness. The proposed procedure decouples the marginal pointwise variation …

Estimation of the mean and covariance parameters for functional data is a critical task, with
local linear smoothing being a popular choice. In recent years, many scientific domains are …

In the era of climate change, the distribution of climate variables evolves with changes not
limited to the mean value. Consequently, clustering algorithms based on central tendency …

Analyzing the real impact of spatial dependency in financial time series data is crucial to
financial risk management. It has been a challenging issue in the last decade. This is …

Biomechanics and human movement research often involves measuring multiple kinematic
or kinetic variables regularly throughout a movement, yielding data that present as smooth …