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Sequential Change Detection and Hypothesis Testing : General Non-i.i.d. Stochastic Models and Asymptotically Optimal Rules

Tartakovsky, Alexander

Edit� par Chapman and Hall/CRC, 2021

ISBN 10 : 1032084359 ISBN 13 : 9781032084350

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Sequential Change Detection and Hypothesis Testing

Alexander Tartakovsky

Edit� par Chapman And Hall/CRC Jun 2021, 2021

ISBN 10 : 1032084359 ISBN 13 : 9781032084350

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -How can major corporations and governments more quickly and accurately detect and address cyberattacks on their networks How can local authorities improve early detection and prevention of epidemics How can researchers improve the identification and classification of space objects in difficult (e.g., dim) settings These questions, among others in dozens of fields, can be addressed using statistical methods of sequential hypothesis testing and changepoint detection. This book considers sequential changepoint detection for very general non-i.i.d. stochastic models, that is, when the observed data is dependent and non-identically distributed. Previous work has primarily focused on changepoint detection with simple hypotheses and single-stream data. This bookextends the asymptotic theory of change detection to the case of composite hypotheses as well as for multi-stream data when the number of affected streams is unknown. These extensions are more relevant for practical applications, including in modern, complex information systems and networks. These extensions are illustratedusing Markov, hidden Markov, state-space, regression, and autoregression models, and several applications, including near-Earth space informatics and cybersecurity are discussed. This book is aimed at graduate students and researchers in statistics and applied probability who are familiar with complete convergence, Markov random walks, renewal and nonlinear renewal theories, Markov renewal theory, and uniform ergodicity of Markov processes. Key features:Design and optimality properties of sequential hypothesis testing and change detection algorithms (in Bayesian, minimax, pointwise, and other settings)Consideration of very general non-i.i.d. stochastic models that include Markov, hidden Markov, state-space linear and non-linear models, regression, and autoregression modelsMultiple decision-making problems, including quickest change detection-identificationReal-world applications to object detection and tracking, near-Earth space informatics, computer network surveillance and security, and other topics 320 pp. Englisch. N� de r�f. du vendeur 9781032084350

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