Univariate Stable Distributions: Models for Heavy Tailed Data (Springer Series in Operations Research and Financial Engineering) - Tapa dura

Libro 39 de 43: Springer Series in Operations Research and Financial Engineering

Nolan, John P.

 
9783030529147: Univariate Stable Distributions: Models for Heavy Tailed Data (Springer Series in Operations Research and Financial Engineering)
Tapa dura
ISBN 10: 3030529142 ISBN 13: 9783030529147
Editorial: Springer-Verlag GmbH, 2020

Sinopsis

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.

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Acerca del autor

​John Nolan received his PhD from the University of Virginia, and has taught at the University of Zambia, Kenyon College, and American University. He also worked in a software firm, developing systems for intensive care units. His main research interests are in models for heavy tailed data and extremes.

De la contraportada

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.

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Editorial
Springer-Verlag GmbH
Año de publicación
2020
Idioma
Inglés
ISBN 10 
3030529142
ISBN 13 
9783030529147
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
348
Contacto del fabricante
Springer Nature Customer Service Center GmbH
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Heidelberg
69115
Alemania

Otras ediciones populares con el mismo título

9783030529178: Univariate Stable Distributions: Models for Heavy Tailed Data (Springer Series in Operations Research and Financial Engineering)

Edición Destacada

ISBN 10:  3030529177 ISBN 13:  9783030529178
Editorial: Springer-Verlag GmbH, 2021
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