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＜電子ブック＞
Statistics and Finance : An Introduction / by David Ruppert
(Springer Texts in Statistics. ISSN:21974136)

版	1st ed. 2004.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2004
本文言語	英語
大きさ	XXII, 474 p : online resource
著者標目	*Ruppert, David author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Social sciences -- Mathematics  全ての件名で検索 FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Mathematics in Business, Economics and Finance FREE:Statistical Theory and Methods
一般注記	Introduction -- Probability and Statistical Models -- Returns -- Time Series Models -- Portfolio Theory -- Regression -- The Capital Asset Pricing Model -- Options Pricing -- Fixed Income Securities -- Resampling -- Value-at-Risk -- GARCH models -- Nonparametric Regression and Splines -- Behavioral Finance This textbook emphasizes the applications of statistics and probability to finance. Students are assumed to have had a prior course in statistics, but no background in finance or economics. The basics of probability and statistics 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 such as portfolio theory, CAPM, and the Black-Scholes formula, and it introduces the somewhat 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 in statistics, engineering, and applied mathematics as well as quantitatively oriented MBA students. Those in the finance industry wishing to know more statistics could also use it for self-study. 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 HTTP:URL=https://doi.org/10.1007/978-1-4419-6876-0

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