Date: Wednesday, March 30, 2022
Location: Zoom Virtual (4:00 PM to 5:00 PM)
Title: Quickest realtime detection of a Brownian coordinate drift
Abstract: Consider the motion of a Brownian particle in two or more dimensions, whose coordinate processes are standard Brownian motions with zero drift initially, and then at some random/unobservable time, one of the coordinate processes gets a (known) nonzero drift permanently. Given that the position of the Brownian particle is being observed in real time, the problem is to detect the time at which a coordinate process gets the drift as accurately as possible. We solve this problem in the most uncertain scenario when the random/unobservable time is (i) exponentially distributed and (ii) independent from the initial motion without drift. The solution is expressed in terms of a stopping time that minimizes the probability of a false early detection and the expected delay of a missed late detection. To our knowledge this is the first time that such a problem has been solved exactly in the literature.
This paper is joint work with Goran Peskir (The University of Manchester) and is currently in press at The Annals of Applied Probability
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Speaker: Philip Ernst
Institution: Rice University
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