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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…mehr

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Produktbeschreibung
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.

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Autorenporträt
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.