Multivariate Statistical Methods
Going Beyond the Linear
-
- USD 89.99
-
- USD 89.99
Descripción editorial
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.
Random Toeplitz Functionals and Their Applications
2025
From Nonparametric Regression to Statistical Inference for Non-Ergodic Diffusion Processes
2025
Sharp Inequalities for Ordered Random Variables in Statistics and Reliability
2024
Introduction to the Statistics of Poisson Processes and Applications
2023
Statistical Analysis of Microbiome Data
2021
Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry
2016