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Univariate stable distributions = mo...

Nolan, John P.

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Univariate stable distributions = models for heavy tailed data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Univariate stable distributions/ by John P. Nolan.
其他題名:	models for heavy tailed data /
作者:	Nolan, John P.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xv, 333 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Multivariate analysis. -
電子資源:	https://doi.org/10.1007/978-3-030-52915-4
ISBN:	9783030529154

Univariate stable distributions = models for heavy tailed data /
Nolan, John P.

Univariate stable distributionsmodels for heavy tailed data /[electronic resource] :by John P. Nolan. - Cham :Springer International Publishing :2020. - xv, 333 p. :ill., digital ;24 cm. - Springer series in operations research and financial engineering,1431-8598. - Springer series in operations research and financial engineering..

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.

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.

ISBN: 9783030529154

Standard No.: 10.1007/978-3-030-52915-4doiSubjects--Topical Terms:

517467
Multivariate analysis.

LC Class. No.: QA278 / .N653 2020

Dewey Class. No.: 519.53

Univariate stable distributions = models for heavy tailed data /
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520 $a 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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