CRC Press Sequential Analysis: Hypothesis Testing and Changepoint Detection

This comprehensive text systematically develops the theory behind sequential hypothesis testing and quickest changepoint detection. It serves as a vital resource for understanding how theoretical results can be applied efficiently in real-world scenarios. The book reviews recent accomplishments in both decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts, offering a broad perspective on modern statistical methods. Authors address not only traditional binary hypotheses but also explore substantially more difficult multiple decision problems. Readers will find detailed coverage of complex scenarios that often arise in engineering and applied probability. Published by Chapman and Hall/CRC as part of their renowned Monographs on Statistics and Applied Probability series, this work bridges the gap between abstract theory and practical application. It is designed for researchers and practitioners who need to master advanced techniques for data analysis and decision-making under uncertainty. The content provides a clear path through intricate statistical challenges without losing focus on usability. Whether you are studying engineering, mathematics, or statistics, this volume offers the depth required to tackle advanced problems in hypothesis testing and change detection.