<電子ブック>
Multivariate Statistical Methods : Going Beyond the Linear / by György Terdik
(Frontiers in Probability and the Statistical Sciences. ISSN:26249995)
版 | 1st ed. 2021. |
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出版者 | (Cham : Springer International Publishing : Imprint: Springer) |
出版年 | 2021 |
大きさ | XIV, 418 p : online resource |
著者標目 | *Terdik, György author SpringerLink (Online service) |
件 名 | LCSH:Statistics LCSH:Mathematical statistics—Data processing FREE:Statistical Theory and Methods FREE:Statistics and Computing |
一般注記 | Some Introductory Algebra -- Tensor derivative of vector functions -- T-Moments and T-Cumulants -- Gaussian systems, T-Hermite polynomials, Moments and Cumulants -- Multivariate Skew Distributions -- Multivariate skewness and kurtosis This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations. Multivariate nonlinear statistical models require the study of higher-order moments and cumulants. The main tool used for the definitions is the tensor derivative, leading to several useful expressions concerning Hermite polynomials, moments, cumulants, skewness, and kurtosis. A general test of multivariate skewness and kurtosis is obtained from this treatment. Exercises are provided for each chapter to help the readers understand the methods. Lastly, the book includes a comprehensive list of references, equipping readers to explore further on their own HTTP:URL=https://doi.org/10.1007/978-3-030-81392-5 |
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電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
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電子ブック | オンライン | 電子ブック |
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Springer eBooks | 9783030813925 |
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電子リソース |
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EB00200938 |