Articles liés à Statistics and Data Analysis for Financial Engineering
Synopsis
Book by Ruppert David
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Biographie de l'auteur
David Ruppert is Andrew Schultz, Jr. Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. His research areas include asymptotic theory, semiparametric regression, functional data analysis, biostatistics, model calibration, measurement error, and astrostatistics. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Electronic Journal of Statistics, former Editor of the Institute of Mathematical Statistics' Lecture Notes-Monographs Series, and former Associate Editor of several major statistics journals. Professor Ruppert has published over 100 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction.
Présentation de l'éditeur
Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is helpful.
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- ÉditeurSpringer
- Date d'édition2012
- ISBN 10 1461427495
- ISBN 13 9781461427490
- ReliureBroché
- Langueanglais
- Nombre de pages664
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Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)
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Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)
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Statistics and Data Analysis for Financial Engineering
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.Some exposure to finance is helpful. 660 pp. Englisch. N° de réf. du vendeur 9781461427490
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Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)
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Statistics and Data Analysis for Financial Engineering
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Examples using financial markets and economic data illustrate important conceptsR Labs with real-data exercises give students practice in data analysisIntegration of graphical and analytic methods for model selection and model checking quan. N° de réf. du vendeur 4197731
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Statistics and Data Analysis for Financial Engineering
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Financial engineers have access to enormous quantities of data but need powerful methods for extracting quantitative information, particularly about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic data, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author's undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in several important aspects: it is graduate-level; computations and graphics are done in R; and many advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are basic statistics and probability, matrices and linear algebra, and calculus.Some exposure to finance is helpful. N° de réf. du vendeur 9781461427490
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Statistics and Data Analysis for Financial Engineering
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Statistics and Data Analysis for Financial Engineering (Springer Texts in Statistics)
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ISBN 10 : 1461427495
ISBN 13 : 9781461427490
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