Are deviations in a gradually varying mean relevant? A testing approach based on sup-norm estimators
DOI10.1214/21-AOS2098zbMath1486.62238arXiv2002.06143OpenAlexW3006614928MaRDI QIDQ2073724
Axel Bücher, Florian Heinrichs, Dette, Holger
Publication date: 7 February 2022
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2002.06143
maximum deviationGumbel distributionGaussian approximationgradual changeslocal-linear estimatorrelevant change point analysis
Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonparametric simultaneous testing for structural breaks
- Testing structural change in time-series nonparametric regression models
- Local linear quantile estimation for nonstationary time series
- Confidence bands in nonparametric time series regression
- Nonparametric inference of quantile curves for nonstationary time series
- Jump-preserving regression and smoothing using local linear fitting: a compromise
- Probabilities of maximal deviations for nonparametric regression function estimates
- Change-points in nonparametric regression analysis
- Testing for changes in multivariate dependent observations with an application to temperature changes
- Kernel-type estimators of jump points and values of a regression function
- Change point estimation using nonparametric regression
- Bias-corrected quantile regression estimation of censored regression models
- Simultaneous nonparametric regression analysis of sparse longitudinal data
- Weak convergence and empirical processes. With applications to statistics
- Evaluating stationarity via change-point alternatives with applications to fMRI data
- Tail-greedy bottom-up data decompositions and fast multiple change-point detection
- Nonparametric statistical procedures for the changepoint problem
- Detecting relevant changes in the mean of nonstationary processes -- a mass excess approach
- Detecting gradual changes in locally stationary processes
- Structural breaks in time series
- Inference for single and multiple change-points in time series
- Testing Statistical Hypotheses of Equivalence and Noninferiority
- UNIFORM CONVERGENCE RATES FOR KERNEL ESTIMATION WITH DEPENDENT DATA
- Improvement of Kernel Type Density Estimators
- Bias-corrected Confidence Bands in Nonparametric Regression
- Image Processing and Jump Regression Analysis
- Bootstrap test for change-points in nonparametric regression
- Multiscale change point detection for dependent data
- Inference of Trends in Time Series
- Variance Change Point Detection Under a Smoothly-Changing Mean Trend with Application to Liver Procurement
- Narrowest-Over-Threshold Detection of Multiple Change Points and Change-Point-Like Features
- Simultaneous Confidence Bands in Nonlinear Regression Models with Nonstationarity
- Heteroscedasticity and Autocorrelation Robust Structural Change Detection
- Detection of Changes in Multivariate Time Series With Application to EEG Data
- Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
- Nonlinear system theory: Another look at dependence
- Multiscale Change Point Inference
- On confidence bands for multivariate nonparametric regression
This page was built for publication: Are deviations in a gradually varying mean relevant? A testing approach based on sup-norm estimators