Statistics and Finance: An Introduction (Springer Texts in Statistics)

Ruppert, David

ISBN 10: 0387202706 / ISBN 13: 9780387202709

Verlag: Springer, 2004

Beschreibung

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Bestandsnummer des Verkäufers Scanned0387202706

Bibliografische Details

• Titel: Statistics and Finance: An Introduction (Springer Texts in Statistics)
• Verlag: Springer
• Erscheinungsjahr: 2004
• Sprache: Englisch
• ISBN-10: 0387202706
• ISBN-13: 9780387202709
• Einband: Hardcover
• Zustand: new

Über diesen Titel

Inhaltsangabe

This book emphasizes the applications of statistics and probability to finance. The basics of these subjects are reviewed and more advanced topics in statistics, such as regression, ARMA and GARCH models, the bootstrap, and nonparametric regression using splines, are introduced as needed. The book covers the classical methods of finance and it introduces the newer area of behavioral finance. Applications and use of MATLAB and SAS software are stressed. The book will serve as a text in courses aimed at advanced undergraduates and masters students. Those in the finance industry can use it for self-study.

Über die Autorin bzw. den Autor

David Ruppert is the Andrew Schultz, Jr. Professor of Engineering, School of Operations Research and Industrial Engineering, Cornell University.  He received a PhD in Statistics from Michigan State University in 1977 and taught for ten years in the Department of Statistics at the University of North Carolina at Chapel Hill.  He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and a winner of the Wilcoxon Prize for the best practical applications paper in Technometrics.  He is former Editor of the Institute of Mathematical Statistics's Lecture Notes-Monographs Series, former Associate Editor of The American Statistician and The Annals of Statistics, and currently Associate Editor of Biometrics and The Journal of the American Statistical Associate. He has published over 80 scientific papers and three books, Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, and Semiparametric Regression.