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.

ISSN

1935-7524

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

85073555462

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

Included in

Life Sciences Commons

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