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Multivariate Statistical Methods: Going Beyond the Linear - György Terdik

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2022-10-28
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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, cumul ... Visas aprašymas

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Aprašymas

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.

Daugiau informacijos

Autorius György Terdik
Leidėjas Springer Nature Switzerland
Series Frontiers in Probability and the Statistical Sciences
Išleidimo metai 2022
Viršelio tipas Minkšti viršeliai
EAN 9783030813949
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139,74 € 186,32 €