Buch, Englisch, 404 Seiten, Previously published in hardcover, Format (B × H): 178 mm x 235 mm, Gewicht: 734 g
Reihe: Springer Series in Operations Research and Financial Engineering
Probabilistic and Statistical Modeling
Buch, Englisch, 404 Seiten, Previously published in hardcover, Format (B × H): 178 mm x 235 mm, Gewicht: 734 g
Reihe: Springer Series in Operations Research and Financial Engineering
ISBN: 978-1-4419-2024-9
Verlag: Springer
This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models.
Prerequisites for the reader include a prior course in stochastic processes and probability, some statistical background, some familiarity with time series analysis, and ability to use a statistics package. This work will serve second-year graduate students and researchers in the areas of applied mathematics, statistics, operations research, electrical engineering, and economics.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Operations Research Spieltheorie
- Mathematik | Informatik Mathematik Stochastik Elementare Stochastik
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
Weitere Infos & Material
Crash Courses.- Crash Course I: Regular Variation.- Crash Course II: Weak Convergence; Implications for Heavy-Tail Analysis.- Statistics.- Dipping a Toe in the Statistical Water.- Probability.- The Poisson Process.- Multivariate Regular Variation and the Poisson Transform.- Weak Convergence and the Poisson Process.- Applied Probability Models and Heavy Tails.- More Statistics.- Additional Statistics Topics.- Appendices.- Notation and Conventions.- Software.
