English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper deals with analyzing structural breaks in the covariance operatorof sequentially observed functional data. For this purpose, procedures aredeveloped to segment an observed stretch of curves into periods for whichsecond-order stationarity may be reasonably assumed. The proposed methods arebased on measuring the fluctuations of sample eigenvalues, either individuallyor jointly, and traces of the sample covariance operator computed from segmentsof the data. To implement the tests, new limit results are introduced that dealwith the large-sample behavior of vector-valued processes built from partialsample eigenvalue estimates. These results in turn enable the calibration ofthe tests to a prescribed asymptotic level. A simulation study and anapplication to Australian annual minimum temperature curves confirm that theproposed methods work well in finite samples. The application suggests that thevariation in annual minimum temperature underwent a structural break in the1950s, after which typical fluctuations from the generally increasingtrendstarted to be significantly smaller.

Structural break analysis for spectrum and trace of covariance operators?