Synopses & Reviews
Extreme value theory (EVT) deals with extreme (rare) events, which are sometimes reported as outliers. Certain textbooks encourage readers to remove outliers in other words, to correct reality if it does not fit the model. Recognizing that any model is only an approximation of reality, statisticians are eager to extract information about unknown distribution making as few assumptions as possible.
Extreme Value Methods with Applications to Finance concentrates on modern topics in EVT, such as processes of exceedances, compound Poisson approximation, Poisson cluster approximation, and nonparametric estimation methods. These topics have not been fully focused on in other books on extremes. In addition, the book covers:
- Extremes in samples of random size
- Methods of estimating extreme quantiles and tail probabilities
- Self-normalized sums of random variables
- Measures of market risk
Along with examples from finance and insurance to illustrate the methods, Extreme Value Methods with Applications to Finance includes over 200 exercises, making it useful as a reference book, self-study tool, or comprehensive course text.
A systematic background to a rapidly growing branch of modern Probability and Statistics: extreme value theory for stationary sequences of random variables.
Synopsis
Focusing on optical imaging problems, applications, and theory, this book describes progress in optical tomography. The text consists of several self-contained chapters with references written by pioneers and experts in the theoretical aspects of optical tomography and nonlinear imaging in general. The first chapter introduces the theoretical problem of optical tomography as it is relevant to applications in biomedical imaging. Each subsequent chapter addresses a particular technique that has been applied to tackle the problem of instability in optical tomography. The last three chapters summarize the state of the art and address the future of the field.
Synopsis
Extreme value theory is a branch of statistics dealing with extreme (rare) events. It provides tools for assessing risk of highly unusual developments, such as financial market crashes.
There has been a huge amount of research in this area during the last 20 years. This book presents a synthesis of that research, with emphasis on dependent observations. It concentrates on modern topics, such as compound Poisson approximation, processes of exceedances, and nonparametric estimation methods, that have not been focused on in other books on extremes.
Along with examples from finance and insurance to illustrate the methods, the book includes over 200 exercises, making it useful as a reference, self-study tool, or comprehensive course text. It is suitable for graduate and postgraduate students in probability and statistics as well as researchers, data analysts, risk managers, and others interested in extreme value theory and its applications.