東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Univariate stable distributions = mo...

Nolan, John P.

Linked to FindBook

Google Book

Amazon

博客來

Univariate stable distributions = models for heavy tailed data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Univariate stable distributions/ by John P. Nolan.
Reminder of title:	models for heavy tailed data /
Author:	Nolan, John P.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xv, 333 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Multivariate analysis. -
Online resource:	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 /
LDR:02842nmm a2200349 a 4500 001 2243868
003 DE-He213
005 20210128085621.0
006 m d
007 cr nn 008maaau
008 211207s2020 sz s 0 eng d
020 $a 9783030529154 $q (electronic bk.)
020 $a 9783030529147 $q (paper)
024 7 $a 10.1007/978-3-030-52915-4 $2 doi
035 $a 978-3-030-52915-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278 $b .N653 2020
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a PBWL $2 thema
082 0 4 $a 519.53 $2 23
090 $a QA278 $b .N787 2020
100 1 $a Nolan, John P. $3 3504305
245 1 0 $a Univariate stable distributions $h [electronic resource] : $b models for heavy tailed data / $c by John P. Nolan.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xv, 333 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in operations research and financial engineering, $x 1431-8598
505 0 $a 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.
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.
650 0 $a Multivariate analysis. $3 517467
650 0 $a Distribution (Probability theory) $3 533179
650 1 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Applications of Mathematics. $3 890893
650 2 4 $a Statistical Theory and Methods. $3 891074
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer series in operations research and financial engineering. $3 1619945
856 4 0 $u https://doi.org/10.1007/978-3-030-52915-4
950 $a Mathematics and Statistics (SpringerNature-11649)