Multivariate Statistical Methods : Going Beyond the Linear / by György Terdik.

Format:

Book

Author/Creator:

Terdik, György, author.

Series:

Frontiers in Probability and the Statistical Sciences, 2624-9995

Language:

English

Subjects (All):

Statistics.

Mathematical statistics--Data processing.

Mathematical statistics.

Statistical Theory and Methods.

Statistics and Computing.

Local Subjects:

Statistical Theory and Methods.

Statistics and Computing.

Physical Description:

1 online resource (424 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references and index.

ISBN:

3-030-81392-4

OCLC:

1283847940