Detecting deviations from second-order stationarity in locally stationary functional time series

DOI10.1007/s10463-019-00721-7OpenAlexW2953013340WikidataQ127681175 ScholiaQ127681175MaRDI QIDQ778883

Axel Bücher, Florian Heinrichs, Dette, Holger

Publication date: 20 July 2020

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.04092

zbMATH Keywords

change pointsCUSUM testalpha mixingautocovariance operatorblock multiplier bootstrap

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Functional data analysis (62R10) Applications of statistics to environmental and related topics (62P12) Hypothesis testing in multivariate analysis (62H15) Monte Carlo methods (65C05)

Related Items (5)

Two-Sample Tests for Relevant Differences in the Eigenfunctions of Covariance Operators ⋮ A new estimation in functional linear concurrent model with covariate dependent and noise contamination ⋮ Factor models for high‐dimensional functional time series I: Representation results ⋮ A portmanteau-type test for detecting serial correlation in locally stationary functional time series ⋮ Detecting structural breaks in eigensystems of functional time series

Uses Software

• freqdom.fda

Cites Work

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• A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing
• Fourier analysis of stationary time series in function space
• Inference for functional data with applications
• Detecting and estimating changes in dependent functional data
• New techniques for empirical processes of dependent data
• Nonparametric regression for locally stationary time series
• Weakly dependent functional data
• Estimation of a change-point in the mean function of functional data
• Testing the stability of the functional autoregressive process
• Fibering method in some probabilistic problems
• Estimates of semiinvariants and centered moments of stochastic processes with mixing. I
• Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes
• Linear processes in function spaces. Theory and applications
• Locally stationary functional time series
• Estimation of mean and covariance operator of autoregressive processes in Banach spaces
• Weak convergence and empirical processes. With applications to statistics
• A note on conditional versus joint unconditional weak convergence in bootstrap consistency results
• A test for second order stationarity of a multivariate time series
• Nonparametric functional data analysis. Theory and practice.
• Forecasting functional time series
• Estimation of mean and covariance operator for Banach space valued autoregressive processes with dependent innovations
• Higher moments of Banach space valued random variables
• A Measure of Stationarity in Locally Stationary Processes With Applications to Testing
• Combining Cumulative Sum Change‐Point Detection Tests for Assessing the Stationarity of Univariate Time Series
• On a measure of lack of fit in time series models
• Automatic Block-Length Selection for the Dependent Bootstrap
• A note on general quadratic forms of nonstationary stochastic processes
• Detecting Changes in the Mean of Functional Observations
• A test for second-order stationarity of a time series based on the discrete Fourier transform
• Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators
• On the Prediction of Stationary Functional Time Series
• Dynamic Functional Principal Components
• A New Non-Parametric Stationarity Test of Time Series in the Time Domain
• Sequential block bootstrap in a Hilbert space with application to change point analysis
• Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models

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