The Annals of Applied Probability

On the Wiener disorder problem

Semih Onur Sezer

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In the Wiener disorder problem, the drift of a Wiener process changes suddenly at some unknown and unobservable disorder time. The objective is to detect this change as quickly as possible after it happens. Earlier work on the Bayesian formulation of this problem brings optimal (or asymptotically optimal) detection rules assuming that the prior distribution of the change time is given at time zero, and additional information is received by observing the Wiener process only. Here, we consider a different information structure where possible causes of this disorder are observed. More precisely, we assume that we also observe an arrival/counting process representing external shocks. The disorder happens because of these shocks, and the change time coincides with one of the arrival times. Such a formulation arises, for example, from detecting a change in financial data caused by major financial events, or detecting damages in structures caused by earthquakes. In this paper, we formulate the problem in a Bayesian framework assuming that those observable shocks form a Poisson process. We present an optimal detection rule that minimizes a linear Bayes risk, which includes the expected detection delay and the probability of early false alarms. We also give the solution of the “variational formulation” where the objective is to minimize the detection delay over all stopping rules for which the false alarm probability does not exceed a given constant.

Article information

Ann. Appl. Probab., Volume 20, Number 4 (2010), 1537-1566.

First available in Project Euclid: 20 July 2010

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Primary: 62L10: Sequential analysis
Secondary: 62L15: Optimal stopping [See also 60G40, 91A60] 62C10: Bayesian problems; characterization of Bayes procedures 60G40: Stopping times; optimal stopping problems; gambling theory [See also 62L15, 91A60]

Sequential change detection jump-diffusion processes optimal stopping


Sezer, Semih Onur. On the Wiener disorder problem. Ann. Appl. Probab. 20 (2010), no. 4, 1537--1566. doi:10.1214/09-AAP655.

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