In a few instances, heuristic arguments are given in addition to the references to the proofs. … A large portion of theoretic results is accompanied by rigorous proofs. While the focus of the book is on the systematic theoretic development of sequential methodology, some recent applications are also covered. "This book gives a comprehensive overview of a wide range of sequential methodology, including both Bayesian and frequentist approaches. Fotopoulos, Washington State University, in Technometrics, July 2017 From my experience, there are not many books of a similar approach I believe it is quite unique in its nature." … The layout of the book is well done and very easy to read. Specifically, it brings theory, visualization, and rich statistical information together to accurately identify the correct model. … This kind of treatment distinguishes this project from its competitors. … 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. 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. … I would be more than thrilled to own this book. 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. It explains how the theoretical aspects influence the hypothesis testing and changepoint detection problems as well as the design of algorithms. 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. Rigorous proofs are given for the most important results. stochastic models in detail, including Markov, hidden Markov, state-space, regression, and autoregression models. 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. They address scenarios with simple hypotheses and more realistic cases of two and finitely many composite hypotheses. The authors not only emphasize traditional binary hypotheses but also substantially more difficult multiple decision problems. The book reviews recent accomplishments in hypothesis testing and changepoint detection both in decision-theoretic (Bayesian) and non-decision-theoretic (non-Bayesian) contexts. It also describes important applications in which theoretical results can be used efficiently. Statist.Sequential Analysis: Hypothesis Testing and Changepoint Detection systematically develops the theory of sequential hypothesis testing and quickest changepoint detection. Siegmund, D.: Some one-sided stopping rules. Williams, E.J., University of Melbourne, Melbourne (1974) Studies in Probability and Statistics, ed. Robbins, H., Siegmund, D.: Sequential estimation of p in Bernoulli trials. Probability and Statistics (Harald Cramer Volume). Robbins, H.: Sequential estimation of the mean of a normal population. Lai, T.L., Siegmund, D.: A nonlinear renewal theory with applications to sequential analysis II. Lai, T.L., Siegmund, D.: A nonlinear renewal theory with applications to sequential analysis I. Gut, A.: On the moments and limit distributions of some first passage times. Berlin-Heidelberg-New York: Springer 1978Ĭhow, Y.S., Yu, K.F.: The performance of a sequential procedure for the estimation of the mean. 48, 600–607 (1952)Ĭhow, Y.S., Studden, W.: Monotonicity of the variance under truncation and variations of Jensen's inequality. Anscombe, F.J.: Large sample theory of sequential estimation.
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