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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://library.oapen.org/handle/20.500.12657/59381
        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
        DOI
        10.1007/978-3-030-95864-0
        ISBN
        9783030958640, 9783030958640
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2022
        Imprint
        Springer International Publishing
        Classification
        Probability and statistics
        Applied mathematics
        Statistical physics
        Pages
        912
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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