Statistics and Data Analysis for Financial Engineering
暫譯: 金融工程的統計與數據分析

Ruppert, David

Statistics and Data Analysis for Financial Engineering

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商品描述

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.

商品描述(中文翻譯)

金融工程師擁有大量的數據，但需要強大的方法來提取定量信息，特別是關於波動性和風險的資訊。本教科書的主要特點包括：用金融市場和經濟數據來說明概念、R Labs 的實際數據練習，以及整合圖形和分析方法來建模和診斷建模錯誤。儘管與作者的本科教科書《Statistics and Finance: An Introduction》有一些重疊，但本書在幾個重要方面與之前的版本有所不同：它是研究生級別的；計算和圖形均在 R 中完成；並且涵蓋了許多進階主題，例如多變量分佈、聯合分佈、貝葉斯計算、VaR 和預期短缺，以及協整。
先修課程包括基本統計學和概率、矩陣和線性代數，以及微積分。
對金融的基本了解會有所幫助。

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, 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.

作者簡介(中文翻譯)

大衛·魯珀特（David Ruppert）是康奈爾大學運籌研究與資訊工程學院的安德魯·舒爾茨（Andrew Schultz, Jr.）工程學教授及統計科學教授，他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（Wilcoxon prize）。他是《電子統計學期刊》（Electronic Journal of Statistics）的編輯，曾擔任數學統計學會的《講義筆記—專著系列》（Lecture Notes--Monographs Series）的編輯，以及多本主要統計學期刊的副編輯。魯珀特教授已發表超過100篇科學論文和四本書籍：《迴歸中的轉換與加權》（Transformation and Weighting in Regression）、《非線性模型中的測量誤差》（Measurement Error in Nonlinear Models）、《半參數迴歸》（Semiparametric Regression）和《統計學與金融：導論》（Statistics and Finance: An Introduction）。

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