A robust method for shift detection in time series

DOI10.1093/biomet/asaa004zbMath1451.62098arXiv1506.03345OpenAlexW3011375837WikidataQ126314770 ScholiaQ126314770MaRDI QIDQ5127211

Roland Fried, Martin Wendler, Herold G. Dehling

Publication date: 21 October 2020

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.03345

zbMATH Keywords

functional central limit theoremweak dependenceHodges-Lehmann estimatorchangepoint testtwo-sample U-statistictwo-sample U-processtwo-sample U-quantile

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10) Nonparametric robustness (62G35) Central limit and other weak theorems (60F05) Functional limit theorems; invariance principles (60F17)

Related Items (4)

Change-point detection based on weighted two-sample U-statistics ⋮ Optimal difference-based variance estimators in time series: a general framework ⋮ Parametric methodologies for detecting changes in maximum temperature of Tlaxco, Tlaxcala, México ⋮ Convergence of the empirical two-sample \(U\)-statistics with \(\beta\)-mixing data

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