Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. It also describes important applications in which theoretical results can be used efficiently.
The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The book primarily focuses on practical discrete-time models, with certain continuous-time models also examined when general results can be obtained very similarly in both cases. It treats both conventional i.i.d. and general non-i.i.d. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. Rigorous proofs are given for the most important results.
Written by leading authorities in the field, this book covers the theoretical developments and applications of sequential hypothesis testing and sequential quickest changepoint detection in a wide range of engineering and environmental domains. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms.
Motivations for the sequential approach. Background on probability and statistics. Sequential Hypothesis Testing: Sequential hypothesis testing—Two simple hypotheses. Sequential hypothesis testing—Multiple simple hypotheses. Sequential hypothesis testing—Composite hypotheses. Change-Point Detection: Statistical models with changes—Problem formulations and optimality criteria. Sequential change-point detection—Bayesian approach. Sequential change-point detection—Non-Bayesian approaches. Multichart change-point detection procedures for composite hypotheses and multipopulation models. Sequential change-point detection and isolation. Applications: Selected applications.
"Tartakovsky’s et al. monograph gives an up-to-date and comprehensive account of its title theme, with both rigorous analysis and description of the subjects in all 11 chapters as a welcome bonus. … I would be more than thrilled to own this book. The authors have selected their topics carefully, have given clear exposition of the methods and their applications, and in some topics they have even illustrated with numeric examples. … The attraction of this book lies in the presentation of original and comprehensive statistical procedures of empirical financial time series that are repeatedly applied to a wide range of theoretical processes. … This kind of treatment distinguishes this project from its competitors. Specifically, it brings theory, visualization, and rich statistical information together to accurately identify the correct model. … The layout of the book is well done and very easy to read. From my experience, there are not many books of a similar approach; I believe it is quite unique in its nature."
—Stergios B. Fotopoulos, Washington State University, in Technometrics, July 2017
"This book gives a comprehensive overview of a wide range of sequential methodology, including both Bayesian and frequentist approaches. While the focus of the book is on the systematic theoretic development of sequential methodology, some recent applications are also covered. … A large portion of theoretic results is accompanied by rigorous proofs. In a few instances, heuristic arguments are given in addition to the references to the proofs. As such, the book is a suitable reference manual for researchers already in the field, for graduate students who plan to write a thesis in a subfield of this topic, as well as for practitioners interested in a comprehensive overview and some deeper insight."
—Mathematical Reviews, August 2015
"The authors of this book are the leading researchers in the field of sequential analysis, and they h