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--- seminars:stat:03122015 [2015/02/07 20:59] – created aleksey
+++ seminars:stat:03122015 [2015/02/07 22:49] (current) – aleksey
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+~~META:title =March 12, 2015~~
+<WRAP 70% center>
+^  **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 |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+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.
+</WRAP>