This textbook highlights the many practical uses of stable distributions exploring the theory
numerical algorithms and statistical methods used to work with stable laws. Because of the
author's accessible and comprehensive approach readers will be able to understand and use
these methods. Both mathematicians and non-mathematicians will find this a valuable resource
for more accurately modelling and predicting large values in a number of real-world
scenarios.Beginning with an introductory chapter that explains key ideas about stable laws
readers will be prepared for the more advanced topics that appear later. The following chapters
present the theory of stable distributions a wide range of applications and statistical
methods with the final chapters focusing on regression signal processing and related
distributions. Each chapter ends with a number of carefully chosen exercises. Links to free
software are included as well where readers can put these methods into practice.Univariate
Stable Distributions is ideal for advanced undergraduate or graduate students in mathematics
as well as many other fields such as statistics economics engineering physics and more. It
will also appeal to researchers in probability theory who seek an authoritative reference on
stable distributions.
