Detecting relevant changes in the mean of nonstationary processes -- a mass excess approach

DOI10.1214/19-AOS1811zbMath1435.62320arXiv1801.09874MaRDI QIDQ2284384

Weichi Wu, Dette, Holger

Publication date: 15 January 2020

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1801.09874

zbMATH Keywords

Gaussian approximationchange point analysislocal linear estimationlocally stationary processrearrangement estimatorsrelevant change points

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) General second-order stochastic processes (60G12) Nonparametric statistical resampling methods (62G09)

Related Items (14)

Box-constrained monotone approximations to Lipschitz regularizations, with applications to robust testing ⋮ Optimal difference-based variance estimators in time series: a general framework ⋮ Modelling time-varying first and second-order structure of time series via wavelets and differencing ⋮ Mean stationarity test in time series: a signal variance-based approach ⋮ Inference for high‐dimensional linear models with locally stationary error processes ⋮ Distribution of Distances based Object Matching: Asymptotic Inference ⋮ Functional Estimation and Change Detection for Nonstationary Time Series ⋮ Comparing time varying regression quantiles under shift invariance ⋮ Identifying shifts between two regression curves ⋮ A distribution free test for changes in the trend function of locally stationary processes ⋮ Rearranged dependence measures ⋮ Detecting relevant changes in the mean of nonstationary processes -- a mass excess approach ⋮ Are deviations in a gradually varying mean relevant? A testing approach based on sup-norm estimators ⋮ Monitoring procedures for strict stationarity based on the multivariate characteristic function

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