000			a
999			_c32480 _d32480
008			230831b xxu||||| |||| 00| 0 eng d
020			_a9781071627914
082			_a519.535 _bREI
100			_aReinsel, Gregory C.
245			_aMultivariate reduced-rank regression : theory, methods and applications
250			_a2nd ed.
260			_bSpringer, _c2022 _aNew York :
300			_axxi, 411 p. ; _bill., _c24 cm
365			_b89.99 _cEUR _d94.90
490			_aLecture notes in statistics ; _vv.225
504			_aIncludes bibliographical references and index.
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.
650			_aMultivariate analysis
650			_aRegression analysis
650			_aProbability and Statistics
700			_aVelu, Rajabather Palani
700			_aChen, Kun
942			_2ddc _cBK