Sequential Analysis: Hypothesis Testing And Changepoint Detection

This book is about sequential hypothesis testing and changepoint detection. It reviews recent advances in these fields, and discusses how the theory affects the testing and detection of hypotheses. It is written by leading authorities in the field, and is suitable for researchers and practitioners working in these areas.

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. Review: This book is a significant leap forward in summarizing and unifying the contemporary approaches and results in the two important directions of sequential analysis: hypothesis testing and change-point detection. This will undoubtedly be the key reference for researchers, practitioners, and perhaps graduate students for the coming decades. -Dr. Michael Baron, Professor, Department of Mathematics and Statistics, American University This book provides a timely consolidation of mathematics and applications relating to hypothesis testing and changepoint detection within sequential analysis. Make no mistake, this book is not suitable for those who have limited understanding of mathematical concepts. The authors have not shied away from including all the mathematical constructs from background knowledge to proofs of complex results. However, everything is conveyed in an easy to follow manner and the book includes a range of realistic examples that cover the complexities a researcher will encounter in real data. This book is set to become a key reference text for anyone working in this area. -Rebecca Killick, Lancaster University