Sequential Change Detection and Hypothesis Testing: General Non-i.i.d. Stochastic Models and Asymptotically Optimal Rules - Paperback

by Alexander Tartakovsky (Author)

Statistical methods for sequential hypothesis testing and changepoint detection have applications across many fields. This book presents an overview of methodology in these related areas, providing a synthesis of research from the last few decades.

Author Biography

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.