Multivariate Statistical Methods: Going Beyond the Linear - György Terdik
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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 ... Pilns apraksts
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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.
Vairāk informācijas
| Autors | György Terdik |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Frontiers in Probability and the Statistical Sciences |
| Izlaides gads | 2022 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783030813949 |