Minimax and pointwise sequential changepoint detection and identification for general stochastic models - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2021

Minimax and pointwise sequential changepoint detection and identification for general stochastic models

Résumé

This paper considers the problem of joint change detection and identification assuming multiple composite postchange hypotheses. We propose a multihypothesis changepoint detection-identification procedure that controls the probabilities of false alarm and wrong identification. We show that the proposed procedure is asymptotically minimax and pointwise optimal, minimizing moments of the detection delay as probabilities of false alarm and wrong identification approach zero. The asymptotic optimality properties hold for general stochastic models with dependent observations. We illustrate general results for detection-identification of changes in multistream Markov ergodic processes. We consider several examples, including an application to rapid detection-identification of COVID-19 in Italy. Our proposed sequential algorithm allows much faster detection of COVID-19 than standard methods.
Fichier principal
Vignette du fichier
PegrTart-10-08-2021.pdf (315.85 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03318733 , version 1 (10-08-2021)

Identifiants

Citer

Serguei M Pergamenchtchikov, Alexander G Tartakovsky, Valentin S Spivak. Minimax and pointwise sequential changepoint detection and identification for general stochastic models. 2021. ⟨hal-03318733⟩
112 Consultations
50 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More