Quickest Detection

The problem of detecting abrupt changes in the behavior of an observed signal or time series arises in a variety of fields, including climate modeling, finance, image analysis, and security. Quickest detection refers to real-time detection of such changes as quickly as possible after they occur. Using the framework of optimal stopping theory, this book describes the fundamentals underpinning the field, providing the background necessary to design, analyze, and understand quickest detection algorithms. For the first time the authors bring together results which were previously scattered across disparate disciplines, and provide a unified treatment of several different approaches to the quickest detection problem. This book is essential reading for anyone who wants to understand the basic statistical procedures for change detection from a fundamental viewpoint, and for those interested in theoretical questions of change detection. It is ideal for graduate students and researchers of engineering, statistics, economics, and finance.

'Nothing endures but change. This timely book applies Heraclitus' fundamental principle to the processes used to describe change itself. As financial markets ebb and flow, practitioners schooled in quickest detection stand the greatest chance for survival.' Peter Carr, Head of Quantitative Financial Research, Bloomberg, 'Quickest Detection is a fascinating read and a nice summary of the state of the art in this area. I would recommend it for use in either coursework or practical engineering.' IEEE Microwave Magazine

H. Vincent Poor is Michael Henry Strater University Professor of Electrical Engineering, and Dean of the School Engineering and Applied Science, at Princeton University, New Jersey, from which he received his PhD in 1977. Prior to joining the Princeton faculty in 1990, he was on the faculty of the University of Illinois, Urbana-Champaign, and has held visiting positions at a number of other institutions, including Imperial College, Harvard University, Massachusetts and Stanford University, California. He is a Fellow of the IEEE, the Institute of Mathematical Statistics, and the American Academy of Arts and Sciences, as well as a member of the US National Academy of Engineering. Olympia Hadjiliadis is an Assistant Professor in the Department of Mathematics at Brooklyn College, City University of New York. She was awarded her M.Math in Statistics and Finance in 1999 from the University of Waterloo, Canada. After receiving her PhD in Statistics with distinction from Columbia University in 2005, Dr Hadjiladis joined the Electrical Engineering Department at Princeton University, New Jersey as a Postdoctoral Fellow, where she is also currently appointed as a Research Collaborator.