Testing Relevant Hypotheses in Functional Time Series via Self-Normalization

DOI10.1111/RSSB.12370OpenAlexW3023883265MaRDI QIDQ5087150

Kevin Kokot, Stanislav Volgushev, Dette, Holger

Publication date: 8 July 2022

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.06092

zbMATH Keywords

change point analysisself-normalizationfunctional time seriestwo-sample problemscumulative sumrelevant hypotheses

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (11)

Detecting relevant differences in the covariance operators of functional time series: a sup-norm approach ⋮ Two-Sample Tests for Relevant Differences in the Eigenfunctions of Covariance Operators ⋮ Rank-based change-point analysis for long-range dependent time series ⋮ Pivotal tests for relevant differences in the second order dynamics of functional time series ⋮ Quantifying deviations from separability in space-time functional processes ⋮ Subsample scan test for multiple breaks based on self-normalization ⋮ Nonasymptotic one- and two-sample tests in high dimension with unknown covariance structure ⋮ Kolmogorov-Smirnov type testing for structural breaks: a new adjusted-range based self-normalization approach ⋮ Detecting relevant changes in the spatiotemporal mean function ⋮ Specification testing in semi-parametric transformation models ⋮ Statistical inference for the slope parameter in functional linear regression

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