E-Book, Englisch, 603 Seiten
Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Hypothesis Testing and Changepoint Detection
E-Book, Englisch, 603 Seiten
Reihe: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
ISBN: 978-1-4398-3821-1
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
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.
Zielgruppe
Researchers and graduate students from statistics, mathematics, engineering, environmental science, econometrics and finance.
Autoren/Hrsg.
Weitere Infos & Material
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
