LEADER 06033nam 22006975 450 001 9910299761903321 005 20220627145510.0 010 $a1-4939-2614-4 024 7 $a10.1007/978-1-4939-2614-5 035 $a(CKB)3710000000403998 035 $a(SSID)ssj0001501740 035 $a(PQKBManifestationID)11968038 035 $a(PQKBTitleCode)TC0001501740 035 $a(PQKBWorkID)11447483 035 $a(PQKB)10093983 035 $a(DE-He213)978-1-4939-2614-5 035 $a(MiAaPQ)EBC5595963 035 $a(PPN)185489982 035 $a(EXLCZ)993710000000403998 100 $a20150421d2015 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aStatistics and Data Analysis for Financial Engineering $ewith R examples /$fby David Ruppert, David S. Matteson 205 $a2nd ed. 2015. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2015. 215 $a1 online resource (XXVI, 719 p. 221 illus., 108 illus. in color.) 225 1 $aSpringer Texts in Statistics,$x1431-875X 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a1-4939-2613-6 327 $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. 330 $aThe 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. 410 0$aSpringer Texts in Statistics,$x1431-875X 606 $aStatistics 606 $aEconomics, Mathematical 606 $aFinance 606 $aR (Computer program language) 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 606 $aQuantitative Finance$3https://scigraph.springernature.com/ontologies/product-market-codes/M13062 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aFinance, general$3https://scigraph.springernature.com/ontologies/product-market-codes/600000 615 0$aStatistics. 615 0$aEconomics, Mathematical. 615 0$aFinance. 615 0$aR (Computer program language) 615 14$aStatistics for Business, Management, Economics, Finance, Insurance. 615 24$aQuantitative Finance. 615 24$aStatistical Theory and Methods. 615 24$aFinance, general. 676 $a332.015195 700 $aRuppert$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut$0102942 702 $aMatteson$b David S$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299761903321 996 $aStatistics and Data Analysis for Financial Engineering$92522536 997 $aUNINA