Sequential Change Detection and Hypothesis Testing
General Non-i.i.d. Stochastic Models and Asymptotically Optimal Rules
1st Edition
Published on 11. December 2019
300 pages
978-0-429-53171-2 (ISBN)
Description
Statistical methods for sequential hypothesis testing and changepoint detection have applications across many fields, including quality control, biomedical engineering, communication networks, econometrics, image processing, security, etc. This book presents an overview of methodology in these related areas, providing a synthesis of research from the last few decades. The methods are illustrated through real data examples, and software is referenced where possible. The emphasis is on providing all the theoretical details in a unified framework, with pointers to new research directions.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
ISBN-13
978-0-429-53171-2 (9780429531712)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Alexander Tartakovsky
Sequential Change Detection and Hypothesis Testing
General Non-i.i.d. Stochastic Models and Asymptotically Optimal Rules
Book
06/2021
1st Edition
Chapman & Hall/CRC
€93.90
Shipment within 10-20 days
Alexander Tartakovsky
Sequential Change Detection and Hypothesis Testing
General Non-i.i.d. Stochastic Models and Asymptotically Optimal Rules
Book
12/2019
1st Edition
Chapman & Hall/CRC
€230.40
Shipment within 15-20 days
Alexander Tartakovsky
Sequential Change Detection and Hypothesis Testing
General Non-I.I.D. Stochastic Models and Asymptotically Optimal Rules
Book
12/2019
CRC Press
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Person
Alexander Tartakovsky is a Professor and Head of the Space Informatics Laboratory at the Moscow Institute of Physics and Technology and President of AGT StatConsult, Los Angeles, California, USA. From 1997 to 2013, he was a Professor at the Department of Mathematics and the Associate Director of the Center for Applied Mathematical Sciences at the University of Southern California, Los Angeles. From 2013 to 2015, he was a Professor at the Department of Statistics at the University of Connecticut at Storrs. He is a fellow of the Institute of Mathematical Statistics and a recipient of the 2007 Abraham Wald Award in Sequential Analysis. His research interests include theoretical and applied statistics, sequential analysis, changepoint detection phenomena, statistical image and signal processing, video surveillance and object detection and tracking, information integration/fusion, cybersecurity, and detection and tracking of malicious activity. He is the author of three books, several book chapters, and over 150 journal and conference publications.
Content
1 Introduction and Motivation; 2 Elements of optimal stopping theory; 3 Changepoint models; 4 Bayesian approaches; 5 The Lorden criterion; 6 Alternative versions of the Lorden criterion; 7 CUSUM for Poisson processes; 8 The maximal probability criterion; 9 The Pollak criterion; 10 Finite time horizon problems;11 Two-sided problems; 12 Quickest detection of changes with composite hypotheses; 13 Quickest change detection in multiple sensors/populations; 14 Performance evaluation; 15 Retrospective change detection; 16 Sequential hypothesis testing; 17 Sequential tests of composite hypotheses; 18 Sequential hypothesis testing in multiple sensors/populations; 19 Sequential Estimation; 20 Applications and Extensions.
