Nonparametric data segmentation in multivariate time series via joint characteristic functions
Authors
Research Topics
Paper Information
-
Journal:
Biometrika -
DOI:
10.1093/biomet/asaf024 -
Published:
March 16, 2025 -
Added to Tracker:
Feb 10, 2026
Abstract
Summary Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial dependence, all without the need to prespecify the type of changes. We show the theoretical consistency of our method in estimating the total number and the locations of the changepoints, and demonstrate its good performance against a variety of changepoint scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.
Author Details
E T McGonigle
AuthorH Cho
AuthorResearch Topics & Keywords
Nonparametric Statistics
Research AreaTime Series
Research AreaCitation Information
APA Format
E T McGonigle
&
H Cho
(2025)
.
Nonparametric data segmentation in multivariate time series via joint characteristic functions.
Biometrika
, 10.1093/biomet/asaf024.
BibTeX Format
@article{paper881,
title = { Nonparametric data segmentation in multivariate time series via joint characteristic functions },
author = {
E T McGonigle
and H Cho
},
journal = { Biometrika },
year = { 2025 },
doi = { 10.1093/biomet/asaf024 },
url = { https://doi.org/10.1093/biomet/asaf024 }
}