Ships from the UK. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects.

Bestandsnummer des Verk�ufers 52022666-20

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Bibliografische Details

Titel: Statistics and Finance : An Introduction
Verlag: Springer New York
Erscheinungsjahr: 2004
Sprache: Englisch
ISBN-10: 0387202706
ISBN-13: 9780387202709
Einband: Hardcover
Zustand: Very Good
Gewicht: 1,91�Pfund

�ber 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.

�ber 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.

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