Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions

DOI10.1214/21-EJS1798zbMath1471.62467arXiv2004.12322OpenAlexW3122724837MaRDI QIDQ2044321

Ghislain Verdier, Ivan Kojadinovic

Publication date: 9 August 2021

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2004.12322

zbMATH Keywords

resamplingonline monitoringasymptotic validity resultsdependent multiplier bootstrapthreshold function estimation

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Applications of statistics to actuarial sciences and financial mathematics (62P05) Hypothesis testing in multivariate analysis (62H15) Nonparametric statistical resampling methods (62G09) Sequential estimation (62L12)

Related Items (3)

Multi‐purpose open‐end monitoring procedures for multivariate observations based on the empirical distribution function ⋮ Open-end nonparametric sequential change-point detection based on the retrospective CUSUM statistic ⋮ Monitoring procedures for strict stationarity based on the multivariate characteristic function

Uses Software

• R
• tseries
• copula
• npcp
• copula
• cpm

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sequential change detection in the presence of unknown parameters
• A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing
• On the reaction time of moving sum detectors
• Financial modeling under non-Gaussian distributions.
• A note on weak convergence of the sequential multivariate empirical process under strong mixing
• Convergence rates in the strong law for bounded mixing sequences
• Sequential testing with uniformly distributed size
• Modified sequential change point procedures based on estimating functions
• The jackknife and the bootstrap for general stationary observations
• On the link between small ball probabilities and the quantization problem for Gaussian measures on Banach spaces
• Monitoring changes in linear models
• Alarm rates for quality control charts
• Weak convergence and empirical processes. With applications to statistics
• Nonparametric tests for change-point detection à la Gombay and Horváth
• A note on conditional versus joint unconditional weak convergence in bootstrap consistency results
• Tests of independence and randomness based on the empirical copula process
• A note on generalized inverses
• Introduction to empirical processes and semiparametric inference
• SEQUENTIAL TESTING FOR THE STABILITY OF HIGH-FREQUENCY PORTFOLIO BETAS
• Asymptotic Theory of Weakly Dependent Random Processes
• Large-sample tests of extreme-value dependence for multivariate copulas
• Combining Cumulative Sum Change‐Point Detection Tests for Assessing the Stationarity of Univariate Time Series
• Asymptotic Statistics
• Monitoring Structural Change
• A Likelihood Ratio Approach to Sequential Change Point Detection for a General Class of Parameters
• Page's sequential procedure for change-point detection in time series regression

This page was built for publication: Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions