SEQUENTIAL CHANGE DETECTION REVISITED

By George V. Moustakides. 2008

63 citations recorded by the source corpus.

In sequential change detection, existing performance measures differ significantly in the way they treat the time of change. By modeling this quantity as a random time, we introduce a general framework capable of capturing and better understanding most well-known criteria and also propose new ones. For a specific new criterion that constitutes an extension to Lorden's performance measure, we offer the optimum structure for detecting a change in the constant drift of a Brownian motion and a formula for the corresponding optimum performance.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/73de827ecbaeec207f417871de1be8b626fb6503

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In Statistics and Probability

Citations in the corpus

Data-efficient minimax quickest change detection (2012, background): “This result was improved upon by Moustakides [7] who showed that the CuSum algorithm is exactly optimal under Lorden’s criterion.”
Numerical Comparison of CUSUM and Shiryaev–Roberts Procedures for Detecting Changes in Distributions (2009, methodology): “two diﬀerent mathematical setups. In the ﬁrst we follow the minimax approach proposed by Lorden (1971) and expressed through (1.1). A similar measure, seemingly less pessimistic (for a discussion see Moustakides, 2008), was proposed in Pollak (1985) where detection speed is expressed via the supremu”