A Portmanteau-type test for detecting serial correlation in locally stationary functional time series
Not on the map. Why?
Abstract (arXiv)
The Portmanteau test provides the vanilla method for detecting serial correlations in classical univariate time series analysis. The method is extended to the case of observations from a locally stationary functional time series. Asymptotic critical values are obtained by a suitable block multiplier bootstrap procedure. The test is shown to asymptotically hold its level and to be consistent against general alternatives.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Sep 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2023Functional Neural Networks: Shift invariant models for functional data with applications to EEG classificationcited by 9
- 2024GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysiscited by 2
- 2025EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselinescited by 1
- 2025EEG-EyeTrack: A Benchmark for Time Series and Functional Data Analysis with Open Challenges and Baselines2 Apr 2025
- 2024GT-PCA: Effective and Interpretable Dimensionality Reduction with General Transform-Invariant Principal Component Analysis28 Jan 2024
- 2023Functional Neural Networks: Shift invariant models for functional data with applications to EEG classification14 Jan 2023