This article is cited in 18 papers

Asymptotic Optimality in Bayesian Changepoint Detection Problems under Global False Alarm Probability Constraint

A. G. Tartakovskii

University of Southern California

Abstract: In the 1960s Shiryaev developed the Bayesian theory of changepoint detection in independent and identically distributed (i.i.d.) sequences. In Shiryaev's classical setting the goal is to minimize an average delay to detection under the constraint imposed on the average probability of false alarm. Recently, Tartakovsky and Veeravalli [Theory Probab. Appl., 49 (2005), pp. 458–497] developed a general Bayesian asymptotic changepoint detection theory (in the classical setting) that is not limited to a restrictive i.i.d. assumption. It was proved that Shiryaev's detection procedure is asymptotically optimal under traditional average false alarm probability constraint, assuming that this probability is small. In the present paper, we consider a less conventional approach where the constraint is imposed on the global, supremum false alarm probability. An asymptotically optimal Bayesian change detection procedure is proposed and thoroughly evaluated for both i.i.d. and non-i.i.d. models when the global false alarm probability approaches zero.

Keywords: asymptotic optimality, changepoint detection, cumulative sum procedure, global false alarm probability, nonlinear renewal theory, Shiryaev's rule, sequential detection.

Received: 11.08.2006

DOI: 10.4213/tvp2443

 English version: Theory of Probability and its Applications, 2009, 53:3, 443–466

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