LEADER 04295nam 22008415 450 001 996495167003316 005 20230421140008.0 010 $a3-030-95864-7 024 7 $a10.1007/978-3-030-95864-0 035 $a(CKB)4920000002044239 035 $a(DE-He213)978-3-030-95864-0 035 $a(MiAaPQ)EBC7105584 035 $a(Au-PeEL)EBL7105584 035 $a(OCoLC)1347381548 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/93966 035 $a(PPN)265860652 035 $a(EXLCZ)994920000002044239 100 $a20221004d2022 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMultivariate Statistical Analysis in the Real and Complex Domains$b[electronic resource] /$fby Arak M. Mathai, Serge B. Provost, Hans J. Haubold 205 $a1st ed. 2022. 210 $aCham$cSpringer Nature$d2022 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (XXVII, 921 p. 3 illus.) 311 $a3-030-95863-9 327 $a1. Mathematical Preliminaries -- 2. The Univariate Gaussian and Related Distribution -- 3. Multivariate Gaussian and Related Distributions -- 4. The Matrix-variate Gaussian Distribution -- 5. Matrix-variate Gamma and Beta Distributions -- 6. Hypothesis Testing and Null Distributions -- 7. Rectangular Matrix-variate Distributions -- 8. Distributions of Eigenvalues and Eigenvectors -- 9. Principal Component Analysis -- 10. Canonical Correlation Analysis -- 11. Factor Analysis -- 12. Classification Problems -- 13. Multivariate Analysis of Variance (MANOVA) -- 14. Profile Analysis and Growth Curves -- 15. Cluster Analysis and Correspondence Analysis. 330 $aThis book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward. This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout. 606 $aMathematical statistics 606 $aStatistics 606 $aMultivariate analysis 606 $aSystem theory 606 $aMathematical Statistics 606 $aStatistical Theory and Methods 606 $aMultivariate Analysis 606 $aComplex Systems 606 $aAnàlisi multivariable$2thub 608 $aLlibres electrònics$2thub 610 $amultivariate statistical analysis 610 $amathematical statistics 610 $acomplex domain 610 $amatrix-variate 610 $aGaussian distributions 610 $aWishart distribution 610 $atype-1 distributions 610 $atype-2 distributions 610 $afactor analysis 610 $aclassifications 610 $acluster 610 $aprofile analyses 615 0$aMathematical statistics. 615 0$aStatistics. 615 0$aMultivariate analysis. 615 0$aSystem theory. 615 14$aMathematical Statistics. 615 24$aStatistical Theory and Methods. 615 24$aMultivariate Analysis. 615 24$aComplex Systems. 615 7$aAnàlisi multivariable 676 $a519.5 700 $aMathai$b Arak M$4aut$4http://id.loc.gov/vocabulary/relators/aut$0868596 702 $aProvost$b Serge B$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHaubold$b Hans J$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996495167003316 996 $aMultivariate Statistical Analysis in the Real and Complex Domains$92995613 997 $aUNISA