Statistics and Data Analysis for Financial Engineering

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's 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.

出版者:Springer

作者:David Ruppert

页数:660

出版时间:2010-11-17

价格:USD 99.00

装帧:Hardcover

isbn号码:9781441977861

图书标签:金融FinanceStatistics金融工程R统计学统计Financial_Engineering

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.

读后感

想看一下，但是英文的看的挺吃力，不知道有没有翻译过来啊，很想学习一下，最近在忙着金融建模，为什么字数还不够啊AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

想看一下，但是英文的看的挺吃力，不知道有没有翻译过来啊，很想学习一下，最近在忙着金融建模，为什么字数还不够啊AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

想看一下，但是英文的看的挺吃力，不知道有没有翻译过来啊，很想学习一下，最近在忙着金融建模，为什么字数还不够啊AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

想看一下，但是英文的看的挺吃力，不知道有没有翻译过来啊，很想学习一下，最近在忙着金融建模，为什么字数还不够啊AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

想看一下，但是英文的看的挺吃力，不知道有没有翻译过来啊，很想学习一下，最近在忙着金融建模，为什么字数还不够啊AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA

用户评价

很不错的书，正在照着写程序

今天算是看完了吧 前面都挺好的 最后两章看的有点废...20180813updated/刚看到ch7开始动脑子看书后速度明显慢了很多????

Stats 509

typos。。。in general a good book. like the R lab section.

非常好