Multivariate Statistical Analysis in the Real and Complex Domains
Springer International Publishing (Verlag)
978-3-030-95866-4 (ISBN)
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.
lt;b>A. M. Mathai is professor emeritus at McGill University and visiting professor at many other universities around the world. He has published over 37 books and over 300 research articles. Dr. Mathai has been invited to speak at conferences, universities, and other institutes around the world. His areas of research include applied statistics, probability, and mathematical statistics.
Serge B. Provost is professor at the University of Western Ontario. His research interests include multivariate analysis, computational statistics and distribution theory, with applications involving problems arising in various areas of scientific investigations such as biostatistics, finance, optics, imaging, and machine learning. Dr. Provost has received three teaching awards and chaired a national fellowship and scholarship selection committee. He is a fellow and chartered statistician of the Royal Statistical Society.
Hans J. Haubold is professor of theoretical astrophysics at the Office for Outer Space Affairs of the United Nations. His research interest focuses on the internal structure of the sun, solar neutrinos, and special functions of mathematical physics. He is also interested in the history of astronomy, physics, and mathematics, specifically Einstein's and Michelson's contributions to theoretical and experimental physics. Dr. Haubold is a member of the American Astronomical Society, the American Mathematical Society, and the History of Science Society.
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.
Erscheinungsdatum | 08.10.2023 |
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Zusatzinfo | XXVII, 921 p. 3 illus. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 210 x 279 mm |
Gewicht | 2266 g |
Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
Mathematik / Informatik ► Mathematik ► Statistik | |
Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
Schlagworte | classifications • Cluster • complex domain • Factor Analysis • Gaussian distributions • Mathematical Statistics • matrix-variate • Multivariate Statistical Analysis • open access • profile analyses • type-1 distributions • type-2 distributions • Wishart Distribution |
ISBN-10 | 3-030-95866-3 / 3030958663 |
ISBN-13 | 978-3-030-95866-4 / 9783030958664 |
Zustand | Neuware |
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