Sequential Analysis: Hypothesis Testing and Changepoint Detection
Sequential Analysis: Hypothesis Testing and Changepoint Detection
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DOI：
10.1201/b17279
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发表时间：
2014-08
期刊：
影响因子：
3
通讯作者：
A. Tartakovsky;I. Nikiforov;M. Basseville
中科院分区：
文献类型：
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作者：
A. Tartakovsky;I. Nikiforov;M. Basseville
顺序分析：假设检验和更改点检测系统地开发了顺序假设检验的理论和最快的更改点检测。它还描述了可以有效地使用理论结果的重要应用。该书回顾了假设检验和更改点检测的最新成就，无论是决策理论（贝叶斯）和非决策理论（非贝拉斯语）上下文。作者不仅强调了传统的二进制假设，而且还强调了更加困难的多重决策问题。他们以简单的假设和两个合成的假设有限的案例来解决方案。该书主要关注实用的离散时间模型，在两种情况下都可以非常相似地获得一般结果时，还检查了某些连续的时间模型。它都对待了常规的I.I.D.和一般非i.i.d。详细的随机模型，包括马尔可夫，隐藏的马尔可夫，州空间，回归和自动回归模型。为最重要的结果提供了严格的证据。本书由该领域的领先当局撰写，涵盖了在广泛的工程和环境领域中的顺序假设测试和顺序最快更换点检测的理论发展和应用。它解释了理论方面如何影响假设检测问题以及算法的设计。
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.