Univariate stable distributions : models for heavy tailed data
Nolan, John P.
Univariate stable distributions : models for heavy tailed data - Cham, Switzerland : Springer, 2020. - xv, 333 p. : illustrations (83 b/w illustrations, 21 illustrations in colour) - Springer Series in Operations Research and Financial Engineering, 1431-8598 .
Includes bibliographical references and index.
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.
9783030529147
Applied mathematics
Distribution (Probability theory)
Engineering mathematics.
Mathematics--Probability & statistics--General
Multivariate analysis
Probability theory and stochastic processes
Brownian motion
Stable laws
Univariate stable distributions
Signal processing algorithm
Pareto distributions
519.25 / NOL-U
Univariate stable distributions : models for heavy tailed data - Cham, Switzerland : Springer, 2020. - xv, 333 p. : illustrations (83 b/w illustrations, 21 illustrations in colour) - Springer Series in Operations Research and Financial Engineering, 1431-8598 .
Includes bibliographical references and index.
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.
9783030529147
Applied mathematics
Distribution (Probability theory)
Engineering mathematics.
Mathematics--Probability & statistics--General
Multivariate analysis
Probability theory and stochastic processes
Brownian motion
Stable laws
Univariate stable distributions
Signal processing algorithm
Pareto distributions
519.25 / NOL-U
