Multivariate Statistical Methods: Going Beyond The Linear eBook

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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.

This is a digital product.

Multivariate Statistical Methods: Going Beyond the Linear is written by György Terdik and published by Springer. The Digital and eTextbook ISBNs for Multivariate Statistical Methods are 9783030813925, 3030813924 and the print ISBNs are 9783030813918, 3030813916.