Multivariate Statistical Analysis in the Real and Complex Domains
Oct 2022 · Springer Nature
4.5star
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About this ebook
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.
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.
Ratings and reviews
4.5
4 reviews
About the author
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.
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.
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