##Statistics Seminar##\\ Department of Mathematical Sciences ~~META:title =March 12, 2015~~ ^ **DATE:**|Thursday, March 12, 2015 | ^ **TIME:**|1:15pm to 2:15pm | ^ **LOCATION:**|WH 100E | ^ **SPEAKER:**|Heng Yang (Graduate Center, City University of New York) | ^ **TITLE:**|Simultaneous detection and identification with post-change uncertainty | \\ **Abstract** We consider the problem of quickest detection of an abrupt change when there is uncertainty about the post-change distribution. Because of the uncertainty, We would like not only detecting the change point but also identifying the post-change distribution simultaneously. In particular we examine this problem in the continuous-time Wiener model where the drift of observations changes from zero to a drift randomly chosen from a collection. We set up the problem as a stochastic optimization in which the objective is to minimize a measure of detection delay subject to a frequency of false alarm constraint, while also identifying the value of the post-change drift up to pre-specified error bounds. We consider a composite rule involving the CUSUM reaction period, that is coupled with an identification function, and show that by choosing parameters appropriately, such a pair of composite rule and identification function can be asymptotically optimal of first order to detect the change point and simultaneously satisfies the error bounds to identify the post-change drift as the average first false alarm increases without bound. We also discuss the detection problem under different situations.