Multiple Change-Point Detection in a Functional Sample via the G-Sum Process

Multiple Change-Point Detection in a Functional Sample via the G-Sum Process

We first define the G-CUSUM process and investigate its theoretical aspects including asymptotic behavior. By choosing different sets G, we propose some tests for multiple change-point detections in a functional sample. We apply the proposed testing procedures to the real-world neurophysiological da...

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Bibliographic Details
Main Authors: Danielius, T. (Author), Račkauskas, A. (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
functional change-point detection
functional data
functional principal component analysis
p-variation
Online Access:View Fulltext in Publisher
LEADER 01095nam a2200193Ia 4500
001 10.3390-math10132294
008 220718s2022 CNT 000 0 und d
020 |a 22277390 (ISSN) 
245 1 0 |a Multiple Change-Point Detection in a Functional Sample via the G-Sum Process 
260 0 |b MDPI  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3390/math10132294 
520 3 |a We first define the G-CUSUM process and investigate its theoretical aspects including asymptotic behavior. By choosing different sets G, we propose some tests for multiple change-point detections in a functional sample. We apply the proposed testing procedures to the real-world neurophysiological data and demonstrate how it can identify the existence of the multiple change-points and localize them. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 
650 0 4 |a functional change-point detection 
650 0 4 |a functional data 
650 0 4 |a functional principal component analysis 
650 0 4 |a p-variation 
700 1 |a Danielius, T.  |e author 
700 1 |a Račkauskas, A.  |e author 
773 |t Mathematics 

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