Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288140 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Time Series Analysis [ISSN:] 1467-9892 [Volume:] 44 [Issue:] 5-6 [Publisher:] John Wiley & Sons, Ltd [Place:] Oxford, UK [Year:] 2023 [Pages:] 505-532
Publisher: 
John Wiley & Sons, Ltd, Oxford, UK
Abstract: 
For a spatiotemporal process {Xj(s,t)∣s∈S,t∈T}j=1,…,n, where S denotes the set of spatial locations and T the time domain, we consider the problem of testing for a change in the sequence of mean functions {μj(s,t)∣s∈S,t∈T}j=1,…,n. In contrast to most of the literature, we are not interested in arbitrarily small changes but only in changes with a norm exceeding a given threshold. Asymptotically distribution free tests are proposed, which do not require the estimation of the long‐run spatiotemporal covariance structure. In particular, we consider a fully functional approach and a test based on the cumulative sum paradigm, investigate the large sample properties of the corresponding test statistics and study their finite sample properties by means of simulation study.
Subjects: 
Spatiotemporal process
functional data analysis
change point analysis
self‐normalization
relevant hypotheses
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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