Statistics and Data Analysis for Financial Engineering: with R examples (Springer Texts in Statistics)
| Author | : | |
| Rating | : | 4.85 (616 Votes) |
| Asin | : | 1493926136 |
| Format Type | : | paperback |
| Number of Pages | : | 719 Pages |
| Publish Date | : | 2016-01-22 |
| Language | : | English |
DESCRIPTION:
Professor Matteson received his PhD in Statistics at the University of Chicago. 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. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics and won the Wilcoxon prize. To make use of these data, the powerful methods in this book, particularly about volatility and risks, are essential. Practicing financial engineers will al
it seems like the author purposefully obfuscates the material because his explanations The book includes concepts that are tremendously valuable, but the author is unable to explain these concepts in a lucid manner. Approximately it seems like the author purposefully obfuscates the material because his explanations Alberto Garciatunon The book includes concepts that are tremendously valuable, but the author is unable to explain these concepts in a lucid manner. Approximately 40% of the book is written in mathematical notation and the author rarely takes the time to define the notation that he uses. At times, it seems like the author purposefully obfuscates the material because his explanations on simple financial concepts are laboriously dense. The author cannot describe simple concepts such as the . 0% of the book is written in mathematical notation and the author rarely takes the time to define the notation that he uses. At times, it seems like the author purposefully obfuscates the material because his explanations on simple financial concepts are laboriously dense. The author cannot describe simple concepts such as the . Stephen B. Futch said Well Worth the Time & Effort. I have spent a number of years working through the 1st Edition. Not only has the book served as a foundation for my understanding in this area, it continues to serve as a ready reference for actual projects I've endeavored. I do not consider myself a mathematical savant so working through the theory is not always easy but it is definitely doable (and, I might add, necessary to have an understanding of what you're actually doing). I would consider Ruppert's approach a n. Amazon customer said Three Stars. Not enough example.
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 asymptotic theory, semiparametric regression, funct
To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. 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. The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. There is an a