This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 634
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #307405
Title: Decentralized Multihypothesis Sequential Detection
Author(s): Yan Wang*+ and Yajun Mei
Companies: Georgia Institute of Technology and Georgia Institute of Technology
Address: School of Industrial and Systems Engineering, Atlanta, GA, ,
Keywords: unambiguous likelihood quantizers ; maximin quantizers ; decentralized sequential detection
Abstract:

In decentralized sequential hypothesis testing, at each time, each of a set of local sensors receives a raw observation and then sends a message to a fusion center which makes a final decision when observations are stopped. Due to limitations of channel bandwidth, it is required that the sensor messages belong to a finite alphabet, possibly binary. In this article, we assume that the raw observations are distributed according to one of M >= 2 pre-specified probability measures, and the fusion center has to utilize quantized sensor messages to decide which one is the true distribution. A main challenge is how to find good quantizers at the local sensors so as to achieve the overall efficiency. Asymptotically Bayes decentralized sequential tests are developed via a two-stage procedure, and the concept of "maximin quantizers" is introduced and analyzed in detail.


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