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. 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