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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 book
extends 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 illustrated
using 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 models Multiple decision-making problems, including
quickest change detection-identification Real-world applications to
object detection and tracking, near-Earth space informatics,
computer network surveillance and security, and other topics
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 book
extends 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 illustrated
using 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 models Multiple decision-making problems, including
quickest change detection-identification Real-world applications to
object detection and tracking, near-Earth space informatics,
computer network surveillance and security, and other topics
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.
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.
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