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＜電子ブック＞
Multivariate Statistical Analysis in the Real and Complex Domains / by Arak M. Mathai, Serge B. Provost, Hans J. Haubold

版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
大きさ	XXVII, 921 p. 3 illus : online resource
著者標目	*Mathai, Arak M author Provost, Serge B author Haubold, Hans J author SpringerLink (Online service)
件　名	LCSH:Mathematical statistics LCSH:Statistics  LCSH:Multivariate analysis LCSH:System theory FREE:Mathematical Statistics FREE:Statistical Theory and Methods FREE:Multivariate Analysis FREE:Complex Systems
一般注記	1. 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 Open Access This 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 HTTP:URL=https://doi.org/10.1007/978-3-030-95864-0

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