Buch, Englisch, 588 Seiten, HC runder Rücken kaschiert, Format (B × H): 183 mm x 260 mm, Gewicht: 1324 g
Reihe: Springer Texts in Statistics
Buch, Englisch, 588 Seiten, HC runder Rücken kaschiert, Format (B × H): 183 mm x 260 mm, Gewicht: 1324 g
Reihe: Springer Texts in Statistics
ISBN: 978-1-4614-8787-6
Verlag: Springer
This is the new, fully-revised edition to the book Statistical Analysis of Financial Data in S-Plus.
René Carmona is the Paul M. Wythes '55 Professor of Engineering and Finance at Princeton University in the department of Operations Research and Financial Engineering, and Director of Graduate Studies of the Bendheim Center for Finance. His publications include over one hundred articles and eight books in probability and statistics. He was elected Fellow of the Institute of Mathematical Statistics in 1984, and of the Society for Industrial and Applied Mathematics in 2010. He is on the editorial board of several peer-reviewed journals and book series. Professor Carmona has developed computer programs for teaching statistics and research in signal analysis and financial engineering. He has worked for many years on energy, the commodity markets and more recently in environmental economics, and he is recognized as a leading researcher and expert in these areas.
Zielgruppe
Upper undergraduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Finanz- und Versicherungsmathematik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines
Weitere Infos & Material
Univariate Data Distributions.- Heavy Tail Distributions.- Dependence and Multivariate Data Exploration.- Parametric Regression.- Local and Nonparametric Regression.- Time Series Models.- Multivariate Time Series, Linear Systems and Kalman Filtering.- Nonlinear Time Series: Models and Simulation.- Appendices.- Indices.