國立虎尾科技大學 |

Univariate Stable Distributions = Models for Heavy Tailed Data /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Univariate Stable Distributions/ by John P. Nolan.
Reminder of title:	Models for Heavy Tailed Data /
Author:	Nolan, John P.
Description:	XV, 333 p. 104 illus., 21 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistical Theory and Methods. -
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. - 1st ed. 2020. - XV, 333 p. 104 illus., 21 illus. in color.online resource. - 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:

671396
Statistical Theory and Methods.

LC Class. No.: QA273.A1-274.9

Dewey Class. No.: 519.2

Univariate Stable Distributions = Models for Heavy Tailed Data /
LDR:03260nam a22004335i 4500 001 1028943
003 DE-He213
005 20200912235917.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030529154 $9 978-3-030-52915-4
024 7 $a 10.1007/978-3-030-52915-4 $2 doi
035 $a 978-3-030-52915-4
050 4 $a QA273.A1-274.9
050 4 $a QA274-274.9
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 519.2 $2 23
100 1 $a Nolan, John P. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1325542
245 1 0 $a Univariate Stable Distributions $h [electronic resource] : $b Models for Heavy Tailed Data / $c by John P. Nolan.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XV, 333 p. 104 illus., 21 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Applications of Mathematics. $3 669175
650 1 4 $a Probability Theory and Stochastic Processes. $3 593945
650 0 $a Statistics . $3 1253516
650 0 $a Engineering mathematics. $3 562757
650 0 $a Applied mathematics. $3 1069907
650 0 $a Probabilities. $3 527847
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030529147
776 0 8 $i Printed edition: $z 9783030529161
776 0 8 $i Printed edition: $z 9783030529178
830 0 $a Springer Series in Operations Research and Financial Engineering, $x 1431-8598 $3 1267183
856 4 0 $u https://doi.org/10.1007/978-3-030-52915-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)