LEADER 03862nam 22005415 450 001 9910633916903321 005 20250326102802.0 010 $a9781071627938 010 $a1071627937 024 7 $a10.1007/978-1-0716-2793-8 035 $a(MiAaPQ)EBC7150604 035 $a(Au-PeEL)EBL7150604 035 $a(CKB)25510411300041 035 $a(PPN)266349285 035 $a(DE-He213)978-1-0716-2793-8 035 $a(EXLCZ)9925510411300041 100 $a20221130d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultivariate Reduced-Rank Regression $eTheory, Methods and Applications /$fby Gregory C. Reinsel, Raja P. Velu, Kun Chen 205 $a2nd ed. 2022. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2022. 215 $a1 online resource (420 pages) $cillustrations 225 1 $aLecture Notes in Statistics,$x2197-7186 ;$v225 311 08$aPrint version: Reinsel, Gregory C. Multivariate Reduced-Rank Regression New York, NY : Springer New York,c2023 9781071627914 320 $aIncludes bibliographical references and index. 327 $a1. Multivariate Linear Regression -- 2. Reduced-Rank Regression Model -- 3. Reduced-Rank Regression Models with Two Sets of Regressors -- 4. Reduced-Rank Regression Model with Autoregressive Errors -- 5. Multiple Time Series Modeling with Reduced Ranks -- 6. The Growth Curve Model and Reduced-Rank Regression Methods -- 7. Seemingly Unrelated Regression Models with Reduced Ranks -- 8. Applications of Reduced-Rank Regression in Financial Economics -- 9. High-Dimensional Reduced-Rank Regression -- 10. Generalized Reduced-Rank Regression with Complex Data -- 11. Sparse and Low-Rank Regression. 12. Alternate Procedures for Analysis of Multivariate Regression Models. 330 $aThis book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed. This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance. This book is designed for advanced students, practitioners, and researchers, who may deal withmoderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering. 410 0$aLecture Notes in Statistics,$x2197-7186 ;$v225 606 $aStatistics 606 $aStatistical Theory and Methods 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 676 $a519.535 686 $aTKK$2ghbs 700 $aReinsel$b Gregory C.$061456 702 $aVelu$b Rajabather Palani 702 $aChen$b Kun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910633916903321 996 $aMultivariate reduced-rank regression$9466543 997 $aUNINA