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.
| Autorius: | György Terdik |
| Serija: | Frontiers in Probability and the Statistical Sciences |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2021 |
| Knygos puslapių skaičius: | 432 |
| ISBN-10: | 3030813916 |
| ISBN-13: | 9783030813918 |
| Formatas: | 241 x 160 x 29 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Multivariate Statistical Methods: Going Beyond the Linear“
