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            Multivariate Statistical Analysis in the Real and Complex Domains

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            Author(s)
            Mathai, Arak M.
            Provost, Serge B.
            Haubold, Hans J.
            Language
            English
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            Abstract
            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.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/175058
            Keywords
            multivariate statistical analysis; mathematical statistics; complex domain; matrix-variate; Gaussian distributions; Wishart distribution; type-1 distributions; type-2 distributions; factor analysis; classifications; cluster; profile analyses; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics; thema EDItEUR::P Mathematics and Science::PH Physics::PHS Statistical physics
            DOI
            10.1007/978-3-030-95864-0
            ISBN
            9783030958640
            Publisher
            Springer Nature
            Publisher website
            http://www.springernature.com/oabooks
            Publication date and place
            Cham, 2022
            Imprint
            Springer International Publishing
            Pages
            912
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            Credits


            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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