Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/318563 
Year of Publication: 
2025
Citation: 
[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 66 [Issue:] 2 [Article No.:] 33 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2025
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those estimated functions are more challenging in models with infinite-dimensional covariates than in regression models with scalar or vector-valued covariates due to a slower rate of convergence of the parameter estimators. Yet the suggested change point test is asymptotically distribution-free and consistent for one-change point alternatives. In the latter case we also show consistency of a change point estimator.
Subjects: 
Change-points
Functional data analysis
Regularized function estimators
Regression
Residual processes
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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