Segmentation of the mean of heteroscedastic data via cross-validation
Segmentation of the mean of heteroscedastic data via cross-validation
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Ithaca: Cornell University Library, arXiv.org
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English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
This paper tackles the problem of detecting abrupt changes in the mean of a heteroscedastic signal by model selection, without knowledge on the variations of the noise. A new family of change-point detection procedures is proposed, showing that cross-validation methods can be successful in the heteroscedastic framework, whereas most existing proced...
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Full title
Segmentation of the mean of heteroscedastic data via cross-validation
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TN_cdi_proquest_journals_2086333182
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2086333182
Other Identifiers
E-ISSN
2331-8422
DOI
10.48550/arxiv.0902.3977
