Source of Publication
Electronic Journal of Statistics
© 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 , 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.
Institute of Mathematical Statistics
Causal linear process, Change-point, Moving block bootstrap, Sequential empirical process, Time series
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
El Ktaibi, Farid and Ivanoff, B. Gail, "Bootstrapping the empirical distribution of a stationary process with change-point" (2019). All Works. 757.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series