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＜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.
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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