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Using a framework built on functional data analysis, we model observations as a collection of function-valued time sequences available at many sites. Each sequence is modelled as an annual mean function, which may change, plus a sequence of error functions, which are spatially correlated. The tests statistics extend the cumulative sum paradigm to this more complex setting. Their asymptotic distributions are not parameter free because of the spatial dependence but can be effectively approximated by Monte Carlo simulations. The new methodology is applied to precipitation data. Its finite sample performance is assessed by a simulation study.<\/jats:p>","DOI":"10.1111\/rssb.12156","type":"journal-article","created":{"date-parts":[[2016,2,18]],"date-time":"2016-02-18T12:41:22Z","timestamp":1455799282000},"page":"29-50","source":"Crossref","is-referenced-by-count":36,"title":["Detection of Change in the Spatiotemporal Mean Function"],"prefix":"10.1093","volume":"79","author":[{"given":"Oleksandr","family":"Gromenko","sequence":"first","affiliation":[{"name":"Tulane University , New Orleans , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piotr","family":"Kokoszka","sequence":"additional","affiliation":[{"name":"Colorado State University , Fort Collins , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthew","family":"Reimherr","sequence":"additional","affiliation":[{"name":"Pennsylvania State University , University Park , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,2,17]]},"reference":[{"key":"2023021709335866300_rssb12156-cit-0001","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.jmva.2012.03.006","article-title":"Detecting and estimating epidemic changes in dependent functional data","volume":"109","author":"Aston","year":"2012","journal-title":"J. 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