Document Type
Article
Source of Publication
Electronic Journal of Statistics
Publication Date
1-1-2019
Abstract
© 2019, Institute of Mathematical Statistics. All rights reserved. When detecting a change-point in the marginal distribution of a stationary time series, bootstrap techniques are required to determine critical values for the tests when the pre-change distribution is unknown. In this paper, we propose a sequential moving block bootstrap and demonstrate its validity under a converging alternative. Furthermore, we demonstrate that power is still achieved by the bootstrap under a non-converging alternative. We follow the approach taken by Peligrad in [14], and avoid assumptions of mixing, association or near epoch dependence. These results are applied to a linear process and are shown to be valid under very mild conditions on the existence of any moment of the innovations and a corresponding condition of summability of the coefficients.
DOI Link
ISSN
Publisher
Institute of Mathematical Statistics
Volume
13
Issue
2
First Page
3572
Last Page
3612
Disciplines
Life Sciences
Keywords
Causal linear process, Change-point, Moving block bootstrap, Sequential empirical process, Time series
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
El Ktaibi, Farid and Ivanoff, B. Gail, "Bootstrapping the empirical distribution of a stationary process with change-point" (2019). All Works. 757.
https://zuscholars.zu.ac.ae/works/757
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series