A new approximation method for the log-likelihood ratio allows robust sequential change-point detection in non-Gaussian processes using moments up to order 3s.
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CHASM detects changes in temporal and cross-variable dependence in multivariate time series by monitoring the truncated eigenvalue sequence of a recursively estimated DMD operator, using optimal assignment and augmented monitoring for complex values.
Kaplan-Meier-based non-parametric estimators for ARL and ADD in quickest changepoint detection are derived with bias bounds and shown to be asymptotically unbiased for finite sequences without extrapolation.
citing papers explorer
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Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection
A new approximation method for the log-likelihood ratio allows robust sequential change-point detection in non-Gaussian processes using moments up to order 3s.
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CHASM: Online Changepoint Detection in Temporal and Cross-Variable Dependence
CHASM detects changes in temporal and cross-variable dependence in multivariate time series by monitoring the truncated eigenvalue sequence of a recursively estimated DMD operator, using optimal assignment and augmented monitoring for complex values.
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Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis
Kaplan-Meier-based non-parametric estimators for ARL and ADD in quickest changepoint detection are derived with bias bounds and shown to be asymptotically unbiased for finite sequences without extrapolation.