001451609 000__ 06228cam\a2200529\a\4500
001451609 001__ 1451609
001451609 003__ OCoLC
001451609 005__ 20230310004708.0
001451609 006__ m\\\\\o\\d\\\\\\\\
001451609 007__ cr\un\nnnunnun
001451609 008__ 221203s2023\\\\nyu\\\\\o\\\\\000\0\eng\d
001451609 020__ $$a9781071627938$$q(electronic bk.)
001451609 020__ $$a1071627937$$q(electronic bk.)
001451609 020__ $$z9781071627914
001451609 0247_ $$a10.1007/978-1-0716-2793-8$$2doi
001451609 035__ $$aSP(OCoLC)1352969340
001451609 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dOCLCF$$dSFB
001451609 049__ $$aISEA
001451609 050_4 $$aQA278.2
001451609 08204 $$a519.5$$223/eng/20221208
001451609 1001_ $$aReinsel, Gregory C.
001451609 24510 $$aMultivariate reduced-rank regression :$$btheory, methods and applications /$$cGregory C. Reinsel, Raja P. Velu, Kun Chen.
001451609 250__ $$a2nd ed.
001451609 260__ $$aNew York, NY :$$bSpringer New York,$$c2023.
001451609 300__ $$a1 online resource (420 p.).
001451609 4901_ $$aLecture Notes in Statistics ;$$vv.225
001451609 500__ $$a5.7.2 Likelihood Ratio Test for the Number of Cointegrating Relations
001451609 5050_ $$aIntro -- Preface to the Second Edition -- Preface to the First Edition -- Contents -- About the Authors -- 1 Multivariate Linear Regression -- 1.1 Introduction -- 1.2 Multivariate Linear Regression Model and Least Squares Estimator -- 1.3 Further Inference Properties in the Multivariate Regression Model -- 1.4 Prediction in the Multivariate Linear Regression Model -- 1.5 Numerical Examples -- 1.5.1 Biochemical Data -- 1.5.2 Sales Performance Data -- 2 Reduced-Rank Regression Model -- 2.1 The Basic Reduced-Rank Model and Background -- 2.2 Some Examples of Application of the Reduced-Rank Model
001451609 5058_ $$a2.3 Estimation of Parameters in the Reduced-Rank Model -- 2.4 Relation to Principal Components and Canonical Correlation Analysis -- 2.4.1 Principal Components Analysis -- 2.4.2 Application to Functional and Structural Relationships Models -- 2.4.3 Canonical Correlation Analysis -- 2.5 Asymptotic Distribution of Estimators in Reduced-Rank Model -- 2.6 Identification of Rank of the Regression Coefficient Matrix -- 2.7 Reduced-Rank Inverse Regression for Estimating Structural Dimension -- 2.8 Numerical Examples -- 2.9 Alternate Procedures for Analysis of Multivariate Regression Models
001451609 5058_ $$a3 Reduced-Rank Regression Models with Two Sets of Regressors -- 3.1 Reduced-Rank Model of Anderson -- 3.2 Application to One-Way ANOVA and Linear Discriminant Analysis -- 3.3 Numerical Example Using Chemometrics Data -- 3.4 Both Regression Matrices of Lower Ranks: Model and Its Applications -- 3.5 Estimation and Inference for the Model -- 3.5.1 Efficient Estimator -- 3.5.2 An Alternative Estimator -- 3.5.3 Asymptotic Inference -- 3.6 Identification of Ranks of Coefficient Matrices -- 3.7 An Example on Ozone Data -- 3.8 Conclusion -- 4 Reduced-Rank Regression Model With Autoregressive Errors
001451609 5058_ $$a4.1 Introduction and the Model -- 4.2 Example on the U.K. Economy: Basic Data and Their Descriptions -- 4.3 Maximum Likelihood Estimators for the Model -- 4.4 Computational Algorithms for Efficient Estimators -- 4.5 Alternative Estimators and Their Properties -- 4.5.1 A Comparison Between Efficient and Other Estimators -- 4.6 Identification of Rank of the Regression Coefficient Matrix -- 4.7 Inference for the Numerical Example -- 4.8 An Alternate Estimator with Kronecker Approximation -- 4.8.1 Computational Results -- 5 Multiple Time Series Modeling With Reduced Ranks
001451609 5058_ $$a5.1 Introduction and Time Series Models -- 5.2 Reduced-Rank Autoregressive Models -- 5.2.1 Estimation and Inference -- 5.2.2 Relationship to Canonical Analysis of Box and Tiao -- 5.3 An Extended Reduced-Rank Autoregressive Model -- 5.4 Nested Reduced-Rank Autoregressive Models -- 5.4.1 Specification of Ranks -- 5.4.2 A Canonical Form -- 5.4.3 Maximum Likelihood Estimation -- 5.5 Numerical Example: U.S. Hog Data -- 5.6 Relationship Between Nonstationarity and Canonical Correlations -- 5.7 Cointegration for Nonstationary Series-Reduced Rank in Long Term -- 5.7.1 LS and ML Estimation and Inference
001451609 506__ $$aAccess limited to authorized users.
001451609 520__ $$aThis book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed. This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance. This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.
001451609 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 8, 2022).
001451609 650_0 $$aRegression analysis.
001451609 655_0 $$aElectronic books.
001451609 7001_ $$aVelu, Rajabather Palani.
001451609 7001_ $$aChen, Kun.
001451609 77608 $$iPrint version:$$aReinsel, Gregory C.$$tMultivariate Reduced-Rank Regression$$dNew York, NY : Springer New York,c2023$$z9781071627914
001451609 830_0 $$aLecture notes in statistics (Springer-Verlag) ;$$vv. 225.
001451609 852__ $$bebk
001451609 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-0716-2793-8$$zOnline Access$$91397441.1
001451609 909CO $$ooai:library.usi.edu:1451609$$pGLOBAL_SET
001451609 980__ $$aBIB
001451609 980__ $$aEBOOK
001451609 982__ $$aEbook
001451609 983__ $$aOnline
001451609 994__ $$a92$$bISE