The Resource Statistics and data analysis for financial engineering, David Ruppert
Statistics and data analysis for financial engineering, David Ruppert
Resource Information
The item Statistics and data analysis for financial engineering, David Ruppert represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Statistics and data analysis for financial engineering, David Ruppert represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
- Summary
- 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. 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
- Language
- eng
- Extent
- 1 online resource (xxii, 638 pages)
- Contents
-
- 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
- Isbn
- 9781441977878
- Label
- Statistics and data analysis for financial engineering
- Title
- Statistics and data analysis for financial engineering
- Statement of responsibility
- David Ruppert
- Language
- eng
- Summary
- 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. 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
- Cataloging source
- GW5XE
- http://library.link/vocab/creatorDate
- 1948-
- http://library.link/vocab/creatorName
- Ruppert, David
- Dewey number
- 658.15
- Illustrations
- illustrations
- Index
- index present
- LC call number
- HG176.7
- LC item number
- .R87 2011
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- Series statement
- Springer texts in statistics
- http://library.link/vocab/subjectName
-
- Financial engineering
- Finance
- Statistics
- Statistics for Business/Economics/Mathematical Finance/Insurance
- Finance
- Label
- Statistics and data analysis for financial engineering, David Ruppert
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- 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
- Control code
- 695386854
- Dimensions
- unknown
- Extent
- 1 online resource (xxii, 638 pages)
- Form of item
- online
- Isbn
- 9781441977878
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-4419-7787-8
- Other physical details
- illustrations.
- http://library.link/vocab/ext/overdrive/overdriveId
- 978-1-4419-7786-1
- Specific material designation
- remote
- System control number
- (OCoLC)695386854
- Label
- Statistics and data analysis for financial engineering, David Ruppert
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- 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
- Control code
- 695386854
- Dimensions
- unknown
- Extent
- 1 online resource (xxii, 638 pages)
- Form of item
- online
- Isbn
- 9781441977878
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-4419-7787-8
- Other physical details
- illustrations.
- http://library.link/vocab/ext/overdrive/overdriveId
- 978-1-4419-7786-1
- Specific material designation
- remote
- System control number
- (OCoLC)695386854
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Statistics-and-data-analysis-for-financial/LSptI3xvMMM/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Statistics-and-data-analysis-for-financial/LSptI3xvMMM/">Statistics and data analysis for financial engineering, David Ruppert</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
