Asymptotically optimal pointwise and minimax change-point detection for general stochastic models with a composite post-change hypothesis

DOI10.1016/j.jmva.2019.104541zbMath1428.62370arXiv1807.09613OpenAlexW2970248573MaRDI QIDQ2008231

Serguei Pergamenchtchikov, Alexander G. Tartakovsky

Publication date: 22 November 2019

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1807.09613

zbMATH Keywords

asymptotic optimalitychangepoint detectionquickest detectioncomposite post-change hypothesisweighted Shiryaev-Roberts procedure

Mathematics Subject Classification ID

Discrete-time Markov processes on general state spaces (60J05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Stopping times; optimal stopping problems; gambling theory (60G40) Sequential statistical analysis (62L10) Optimal stopping in statistics (62L15)

Related Items (6)

Minimax and pointwise sequential changepoint detection and identification for general stochastic models ⋮ Detection and identification of changes of hidden Markov chains: asymptotic theory ⋮ Asymptotically optimal robust information-based quick detection for general stochastic models with nonparametric postchange uncertainty ⋮ Truncated sequential change-point detection for Markov chains with applications in the epidemic statistical analysis ⋮ Asymptotic properties of \(M\)-estimators based on estimating equations and censored data in semi-parametric models with multiple change points ⋮ Asymptotic properties of semiparametric \(M\)-estimators with multiple change points

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