Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics)
暫譯: 金融工程的統計與數據分析：以 R 範例為例（Springer 統計系列）

David Ruppert, David S. Matteson

Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics)

商品描述

The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.

商品描述(中文翻譯)

這本有影響力的教科書的新版本，針對研究生或高年級本科生，教授金融工程所需的統計學。它通過金融市場和經濟數據、R Labs 的實際數據練習，以及用於建模和診斷建模錯誤的圖形和分析方法來說明概念。這些方法至關重要，因為金融工程師現在可以訪問大量數據。為了利用這些數據，本書中處理定量信息的強大方法，特別是關於波動性和風險的部分，是必不可少的。這個全面修訂的版本的優勢包括對 R 代碼的重大補充和涵蓋的高級主題。各章節涵蓋的主題包括多變量分佈、聯合分佈、貝葉斯計算、風險管理和協整等。建議的先修知識包括基本的統計學和概率論、矩陣和線性代數以及微積分。書中還附有概率、統計和線性代數的附錄。實務中的金融工程師也會發現這本書很有趣。

作者簡介

David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Research and Information Engineering and Department of Statistical Science, 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 Journal of the American Statistical Association-Theory and Methods, former editor of the Electronic Journal of Statistics, former Editor of the Institute of Mathematical Statistics's Lecture Notes--Monographs Series and former Associate Editor of several major statistics journals. Professor Ruppert has published over 125 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction.

David S. Matteson is Assistant Professor of Statistical Science, ILR School and Department of Statistical Science, Cornell University, where he is a member of the Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering courses. His research areas include multivariate time series, signal processing, financial econometrics, spatio-temporal modeling, dimension reduction, machine learning, and biostatistics. Professor Matteson received his PhD in Statistics at the University of Chicago and his BS in Finance, Mathematics, and Statistics at the University of Minnesota. He received a CAREER Award from the National Science Foundation and won Best Academic Paper Awards from the annual R/Finance conference. He is an Associate Editor of the Journal of the American Statistical Association-Theory and Methods, Biometrics, and Statistica Sinica. He is also an Officer for the Business and Economic Statistics Section of American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society.

作者簡介(中文翻譯)

大衛·魯珀特是康奈爾大學運籌學與資訊工程學院及統計科學系的安德魯·舒爾茨教授，教授統計學和金融工程，並且是金融工程計畫的成員。他的研究領域包括漸近理論、半參數迴歸、函數型資料分析、生物統計學、模型校準、測量誤差和天文統計學。魯珀特教授在密西根州立大學獲得統計學博士學位。他是美國統計協會和數學統計學會的會士，並獲得威爾科克森獎。他是《美國統計協會期刊-理論與方法》的編輯，曾任《電子統計期刊》的編輯，曾任數學統計學會的講義系列編輯，以及多個主要統計期刊的副編輯。魯珀特教授已發表超過125篇科學論文和四本書籍：《迴歸中的轉換與加權》、《非線性模型中的測量誤差》、《半參數迴歸與統計學》和《金融：導論》。

大衛·S·馬特森是康奈爾大學ILR學院及統計科學系的助理教授，他是應用數學中心、運籌學領域及金融工程計畫的成員，並教授統計學和金融工程課程。他的研究領域包括多變量時間序列、信號處理、金融計量經濟學、時空建模、降維、機器學習和生物統計學。馬特森教授在芝加哥大學獲得統計學博士學位，並在明尼蘇達大學獲得金融、數學和統計學的學士學位。他獲得國家科學基金會的CAREER獎，並在年度R/Finance會議中獲得最佳學術論文獎。他是《美國統計協會期刊-理論與方法》、《生物統計學》和《Statistica Sinica》的副編輯。他還是美國統計協會商業與經濟統計分會的官員，以及數學統計學會和國際生物統計學會的成員。

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