國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Statistics and data analysis for fin...

Matteson, David S.

Statistics and data analysis for financial engineeringwith R examples /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistics and data analysis for financial engineeringby David Ruppert, David S. Matteson.
其他題名:	with R examples /
作者:	Ruppert, David.
其他作者:	Matteson, David S.
出版者:	New York, NY :Springer New York :2015.
面頁冊數:	xxvi, 221 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Financial engineeringStatistical methods.
電子資源:	http://dx.doi.org/10.1007/978-1-4939-2614-5
ISBN:	9781493926145 (electronic bk.)

Statistics and data analysis for financial engineeringwith R examples /
Ruppert, David.

Statistics and data analysis for financial engineeringwith R examples /[electronic resource] :by David Ruppert, David S. Matteson. - 2nd ed. - New York, NY :Springer New York :2015. - xxvi, 221 p. :ill., digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

Introduction -- Returns -- Fixed income securities -- Exploratory data analysis -- Modeling univariate distributions -- Resampling -- Multivariate statistical models -- Copulas -- Time series models: basics -- Time series models: further topics -- Portfolio theory -- Regression: basics -- Regression: troubleshooting -- Regression: advanced topics -- Cointegration -- The capital asset pricing model -- Factor models and principal components -- GARCH models -- Risk management -- Bayesian data analysis and MCMC -- Nonparametric regression and splines.

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. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, 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, multivariate volatility 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. David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science at Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. 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 and former Editor of the Electronic Journal of Statistics and of the Institute of Mathematical Statistics's Lecture Notes—Monographs. 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 at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering. Professor Matteson received his PhD in Statistics at the University of Chicago. 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 the American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society.

ISBN: 9781493926145 (electronic bk.)

Standard No.: 10.1007/978-1-4939-2614-5doiSubjects--Topical Terms:

509945
Financial engineering
--Statistical methods.

LC Class. No.: HG176.7

Dewey Class. No.: 658.15

Statistics and data analysis for financial engineeringwith R examples /
LDR:04454nmm a2200349 a 4500 001 465795
003 DE-He213
005 20151116163520.0
006 m d
007 cr nn 008maaau
008 151222s2015 nyu s 0 eng d
020 $a 9781493926145 (electronic bk.)
020 $a 9781493926138 (paper)
024 7 $a 10.1007/978-1-4939-2614-5 $2 doi
035 $a 978-1-4939-2614-5
040 $a GP $c GP
041 0 $a eng
050 4 $a HG176.7
072 7 $a PBT $2 bicssc
072 7 $a K $2 bicssc
072 7 $a BUS061000 $2 bisacsh
082 0 4 $a 658.15 $2 23
090 $a HG176.7 $b .R946 2015
100 1 $a Ruppert, David. $3 323300
245 1 0 $a Statistics and data analysis for financial engineering $h [electronic resource] : $b with R examples / $c by David Ruppert, David S. Matteson.
250 $a 2nd ed.
260 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2015.
300 $a xxvi, 221 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 1431-875X
505 0 $a Introduction -- Returns -- Fixed income securities -- Exploratory data analysis -- Modeling univariate distributions -- Resampling -- Multivariate statistical models -- Copulas -- Time series models: basics -- Time series models: further topics -- Portfolio theory -- Regression: basics -- Regression: troubleshooting -- Regression: advanced topics -- Cointegration -- The capital asset pricing model -- Factor models and principal components -- GARCH models -- Risk management -- Bayesian data analysis and MCMC -- Nonparametric regression and splines.
520 $a 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. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, 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, multivariate volatility 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. David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science at Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. 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 and former Editor of the Electronic Journal of Statistics and of the Institute of Mathematical Statistics's Lecture Notes—Monographs. 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 at Cornell University, where he is a member of the ILR School, Center for Applied Mathematics, Field of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering. Professor Matteson received his PhD in Statistics at the University of Chicago. 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 the American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society.
650 0 $a Financial engineering $x Statistical methods. $3 509945
650 0 $a Finance $x Statistical methods. $3 240075
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 274062
650 2 4 $a Quantitative Finance. $3 274071
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Finance/Investment/Banking. $3 511434
700 1 $a Matteson, David S. $3 719746
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer texts in statistics. $3 559235
856 4 0 $u http://dx.doi.org/10.1007/978-1-4939-2614-5
950 $a Mathematics and Statistics (Springer-11649)