A Likelihood Ratio Approach to Sequential Change Point Detection for a General Class of Parameters

DOI10.1080/01621459.2019.1630562zbMath1441.62212arXiv1802.07696OpenAlexW2962982950MaRDI QIDQ5120674

Josua Gösmann, Dette, Holger

Publication date: 15 September 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.07696

zbMATH Keywords

change point analysisself-normalizationsequential monitoringlikelihood ratio principle

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Sequential estimation (62L12)

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Cites Work

• Unnamed Item
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• Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation
• Delay times of sequential procedures for multiple time series regression models
• Functional Erdős-Rényi law of large numbers for nonconventional sums under weak dependence
• Monitoring correlation change in a sequence of random variables
• Uniform change point tests in high dimension
• Break detection in the covariance structure of multivariate time series models
• Optimal stopping times for detecting changes in distributions
• Modified sequential change point procedures based on estimating functions
• Monitoring changes in linear models
• Testing for a change in the parameter values and order of an autoregressive model
• On the Bahadur representation of sample quantiles for dependent sequences
• Structural breaks in time series
• Inference for single and multiple change-points in time series
• SEQUENTIAL TESTING FOR THE STABILITY OF HIGH-FREQUENCY PORTFOLIO BETAS
• Change‐point monitoring in linear models
• On a Conjecture of Revesz
• Monitoring Structural Change
• Unsupervised Self-Normalized Change-Point Testing for Time Series
• Testing for Change Points in Time Series
• Page's sequential procedure for change-point detection in time series regression
• Self-Normalization for Time Series: A Review of Recent Developments
• Detecting Relevant Changes in Time Series Models
• Nonlinear system theory: Another look at dependence
• A Note on Quantiles in Large Samples
• Inference about the change-point from cumulative sum tests
• CONTINUOUS INSPECTION SCHEMES

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