LEADER 03484nam 22005415 450 001 9910484711703321 005 20251113185028.0 010 $a3-030-52915-0 024 7 $a10.1007/978-3-030-52915-4 035 $a(CKB)4100000011457936 035 $a(DE-He213)978-3-030-52915-4 035 $a(MiAaPQ)EBC6348986 035 $a(PPN)25022108X 035 $a(MiAaPQ)EBC29092614 035 $a(EXLCZ)994100000011457936 100 $a20200912d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aUnivariate Stable Distributions $eModels for Heavy Tailed Data /$fby John P. Nolan 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XV, 333 p. 104 illus., 21 illus. in color.) 225 1 $aSpringer Series in Operations Research and Financial Engineering,$x2197-1773 311 08$a3-030-52914-2 320 $aIncludes bibliographical references and index. 327 $aBasic 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. 330 $aThis textbook highlights the many practical uses of stable distributions, exploring the theory, numerical algorithms, and statistical methods used to work with stable laws. Because of the author?s accessible and comprehensive approach, readers will be able to understand and use these methods. Both mathematicians and non-mathematicians will find this a valuable resource for more accurately modelling and predicting large values in a number of real-world scenarios. Beginning with an introductory chapter that explains key ideas about stable laws, readers will be prepared for the more advanced topics that appear later. The following chapters present the theory of stable distributions, a wide range of applications, and statistical methods, with the final chapters focusing on regression, signal processing, and related distributions. Each chapter ends with a number of carefully chosen exercises. Links to free software are included as well, where readers can put these methodsinto practice. Univariate Stable Distributions is ideal for advanced undergraduate or graduate students in mathematics, as well as many other fields, such as statistics, economics, engineering, physics, and more. It will also appeal to researchers in probability theory who seek an authoritative reference on stable distributions. 410 0$aSpringer Series in Operations Research and Financial Engineering,$x2197-1773 606 $aMathematical statistics 606 $aProbabilities 606 $aMathematical Statistics 606 $aProbability Theory 615 0$aMathematical statistics. 615 0$aProbabilities. 615 14$aMathematical Statistics. 615 24$aProbability Theory. 676 $a519.53 700 $aNolan$b John$g(John P.),$0874345 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484711703321 996 $aUnivariate stable distributions$92073398 997 $aUNINA