Functional Data Analysis (FDA) has emerged as a powerful framework for datasets that
exhibit continuous variation over specified intervals. Unlike traditional methods, FDA treats …

Functional Ordinary Kriging is the most widely used method to predict a curve at a given
spatial point. However, uncertainty remains an open issue. In this article a distribution-free …

Abstract Spatial Functional Data (SFD) analysis is an emerging statistical framework that
combines Functional Data Analysis (FDA) and spatial dependency modeling. Unlike …

This study presents a Bayesian hierarchical model for analyzing spatially correlated
functional data and handling irregularly spaced observations. The model uses Bernstein …

Registration of multivariate functional data involves handling of both cross-component and
cross-observation phase variations. Allowing for the two phase variations to be modelled as …

Changepoint detection is a vital tool in climate data analysis. Numerous types of climate
observation data are properly represented by functional time series, implying a need for …

Electroencephalography (EEG) signals represent the brain's electrical activity ob tained
through placing electrodes on the scalp. These recordings can be analyzed as curves …

Functional Ordinary Kriging is the most widely used method to predict a curve at a given
spatial point. However, uncertainty remains an open issue. In this article a distribution-free …

Electroencephalography (EEG) signals represent the brain's electrical activity obtained
through placing electrodes on the scalp. These recordings can be analyzed as curves …

The present work aims to classify the thought of the five Spanish vowels measured by
electroencephalograms (EEG) of 21 electrodes around the Broca's area of the brain of 23 …