Multivariate Statistical Methods : Going Beyond the Linear /
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...
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| Format: | eBook |
| Language: | English |
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Cham :
Springer International Publishing : Imprint: Springer,
2021.
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| Edition: | 1st ed. 2021. |
| Series: | Frontiers in Probability and the Statistical Sciences,
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| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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. |
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| Physical Description: | 1 online resource (XIV, 418 pages) |
| ISBN: | 9783030813925 |
| ISSN: | 2624-9995 |
| DOI: | 10.1007/978-3-030-81392-5 |