Univariate Stable Distributions : Models for Heavy Tailed Data
Author(s)
Bibliographic Information
Univariate Stable Distributions : Models for Heavy Tailed Data
（Springer series in operations research and financial engineering）
Springer Nature Switzerland AG, c2020
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Description and Table of Contents
Description
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.
Table of Contents
Basic Properties of Univariate Stable Distributions.- Modeling with Stable Distributions.- Technical Results for Univariate Stable Distributions.- Univariate Estimation.- Stable Regression.- Signal Processing with Stable Distributions.- Related Distributions.- Appendix A: Mathematical Facts.- Appendix B: Stable Quantiles.- Appendix C: Stable Modes.- Appendix D: Asymptotic Standard Deviations of ML Estimators.
by "Nielsen BookData"