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Titlebook: Innovations in Multivariate Statistical Modeling; Navigating Theoretic Andri?tte Bekker,Johannes T. Ferreira,Ding-Geng Ch Book 2022 The Edi

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書(shū)目名稱(chēng)Innovations in Multivariate Statistical Modeling
副標(biāo)題Navigating Theoretic
編輯Andri?tte Bekker,Johannes T. Ferreira,Ding-Geng Ch
視頻videohttp://file.papertrans.cn/468/467157/467157.mp4
概述Features new developments and contemporary innovations originating from multivariate statistical theory.Showcases multidisciplinary approaches and applications, reaching both theoretical and applied s
叢書(shū)名稱(chēng)Emerging Topics in Statistics and Biostatistics
圖書(shū)封面Titlebook: Innovations in Multivariate Statistical Modeling; Navigating Theoretic Andri?tte Bekker,Johannes T. Ferreira,Ding-Geng Ch Book 2022 The Edi
描述.Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics..It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understa
出版日期Book 2022
關(guān)鍵詞Bayesian Network; Bayesian Inference; Data-driven Science, Modeling and Theory Building; Matrix Theory;
版次1
doihttps://doi.org/10.1007/978-3-031-13971-0
isbn_softcover978-3-031-13973-4
isbn_ebook978-3-031-13971-0Series ISSN 2524-7735 Series E-ISSN 2524-7743
issn_series 2524-7735
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Book 2022ave produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple discip
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Modeling Handwritten Digits Dataset Using the Matrix Variate t Distributionthe estimates for the parameters of interests. A real data example illustrates that the matrix variate t distribution can be used as a robust alternative to the matrix variate normal distribution for modeling matrix variate datasets with some atypical observations.
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Some Computational Aspects of?a?Noncentral Dirichlet Familyeir effect on the doubly noncentral Dirichlet, compared to the singly alternative, affects the practical implementation of the model. Real data examples are used for this investigation by using maximum likelihood estimation for the parameters and further strengthened by simulation studies.
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A Flexible Matrix-Valued Response Regression for?Skewed Dataion using the envelope methodology to construct a more parsimonious parameterized model. The model fit is illustrated and compared with a new matrix-variate skew-normal model, as well as a matrix-variate normal model, on both simulated and real examples.
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