Modified sequential change point procedures based on estimating functions

DOI10.1214/18-EJS1431zbMath1392.62241WikidataQ129753521 ScholiaQ129753521MaRDI QIDQ1753154

Silke Weber, Claudia Kirch

Publication date: 28 May 2018

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1527300141

zbMATH Keywords

regressionchange point analysissequential testonline monitoringinteger-valued time series

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Sequential statistical analysis (62L10)

Related Items (17)

Poisson QMLE for change-point detection in general integer-valued time series models ⋮ Sequential change point detection in high dimensional time series ⋮ Asymptotic delay times of sequential tests based on \(U\)-statistics for early and late change points ⋮ Monitoring mean and variance change-points in long-memory time series ⋮ Real-time detection of a change-point in a linear expectile model ⋮ Multi‐purpose open‐end monitoring procedures for multivariate observations based on the empirical distribution function ⋮ A Likelihood Ratio Approach to Sequential Change Point Detection for a General Class of Parameters ⋮ Two-stage data segmentation permitting multiscale change points, heavy tails and dependence ⋮ A Note on Online Change Point Detection ⋮ Nonparametric sequential change-point detection for multivariate time series based on empirical distribution functions ⋮ Open-end nonparametric sequential change-point detection based on the retrospective CUSUM statistic ⋮ Sequential change point test in the presence of outliers: the density power divergence based approach ⋮ Anomaly detection: a functional analysis perspective ⋮ Monitoring procedures for strict stationarity based on the multivariate characteristic function ⋮ A new approach for open‐end sequential change point monitoring ⋮ Online non-parametric changepoint detection with application to monitoring operational performance of network devices ⋮ Inference for nonstationary time series of counts with application to change-point problems

Uses Software

• strucchange

Cites Work

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