Universität zu Köln

Bucchia, Beatrice and Wendler, Martin (2017). Change-point detection and bootstrap for Hilbert space valued random fields. J. Multivar. Anal., 155. S. 344 - 369. SAN DIEGO: ELSEVIER INC. ISSN 0047-259X

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Abstract

The problem of testing for the presence of epidemic changes in random fields is investigated. In order to be able to deal with general changes in the marginal distribution, a Cramer-von Mises type test is introduced which is based on Hilbert space theory. A functional central limit theorem for p-mixing Hilbert space valued random fields is proven. In order to avoid the estimation of the long-run variance and obtain critical values, Shao's dependent wild bootstrap method is adapted to this context. For this, a joint functional central limit theorem for the original and the bootstrap sample is shown. Finally, the theoretic results are supplemented by a short simulation study. (C) 2017 Elsevier Inc. All rights reserved.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Bucchia, Beatrice UNSPECIFIED UNSPECIFIED UNSPECIFIED Wendler, Martin UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-238508
DOI:	10.1016/j.jmva.2017.01.007
Journal or Publication Title:	J. Multivar. Anal.
Volume:	155
Page Range:	S. 344 - 369
Date:	2017
Publisher:	ELSEVIER INC
Place of Publication:	SAN DIEGO
ISSN:	0047-259X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language CENTRAL-LIMIT-THEOREM; STOCHASTIC-PROCESSES; BLOCKWISE BOOTSTRAP; EMPIRICAL PROCESSES; WEAK-CONVERGENCE; RANDOM-VARIABLES; INEQUALITIES; DEPENDENCE Multiple languages Statistics & Probability Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/23850