000 04078nam a22004455i 4500
001 978-1-4419-7787-8
003 DE-He213
005 20140220083725.0
007 cr nn 008mamaa
008 101109s2011 xxu| s |||| 0|eng d
020 _a9781441977878
_9978-1-4419-7787-8
024 7 _a10.1007/978-1-4419-7787-8
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aK
_2bicssc
072 7 _aBUS061000
_2bisacsh
082 0 4 _a330.015195
_223
100 1 _aRuppert, David.
_eauthor.
245 1 0 _aStatistics and Data Analysis for Financial Engineering
_h[electronic resource] /
_cby David Ruppert.
264 1 _aNew York, NY :
_bSpringer New York,
_c2011.
300 _aXXII, 638 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Texts in Statistics,
_x1431-875X
505 0 _aIntroduction -- 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 _aFinancial 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.
650 0 _aStatistics.
650 0 _aEconomics
_xStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441977861
830 0 _aSpringer Texts in Statistics,
_x1431-875X
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-7787-8
912 _aZDB-2-SMA
999 _c105858
_d105858