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

✍ Scribed by Alexander Tartakovsky (Author)

• Publisher: Chapman and Hall/CRC
• Year: 2019
• Leaves: 321
• Edition: 1
• Category: Library

✦ Synopsis

Statistical methods for sequential hypothesis testing and changepoint detection have applications across many fields, including quality control, biomedical engineering, communication networks, econometrics, image processing, security, etc. This book presents an overview of methodology in these related areas, providing a synthesis of research from the last few decades. The methods are illustrated through real data examples, and software is referenced where possible. The emphasis is on providing all the theoretical details in a unified framework, with pointers to new research directions.

✦ Table of Contents

1 Introduction and Motivation; 2 Elements of optimal stopping theory; 3 Changepoint models; 4 Bayesian approaches; 5 The Lorden criterion; 6 Alternative versions of the Lorden criterion; 7 CUSUM for Poisson processes; 8 The maximal probability criterion; 9 The Pollak criterion; 10 Finite time horizon problems;11 Two-sided problems; 12 Quickest detection of changes with composite hypotheses; 13 Quickest change detection in multiple sensors/populations; 14 Performance evaluation; 15 Retrospective change detection; 16 Sequential hypothesis testing; 17 Sequential tests of composite hypotheses; 18 Sequential hypothesis testing in multiple sensors/populations; 19 Sequential Estimation; 20 Applications and Extensions.