Multivariate, Multilinear and Mixed Linear Models

The book provides the latest findings on statistical inference in multivariate, multilinear, and mixed linear models, covering identification and testing of covariance structures and means, estimation in mixed linear models, and an extensive bibliography. It is intended for PhD students and researchers in modern statistical science.

Format: Paperback / softback
Length: 350 pages
Publication date: 03 October 2022
Publisher: Springer Nature Switzerland AG

This comprehensive book delves into the latest advancements in statistical inference for multivariate, multilinear, and mixed linear models, offering a holistic perspective on the subject. It comprises pioneering and meticulously selected review contributions from esteemed experts in the field, providing a valuable guide for readers navigating topics related to estimation and testing of multivariate and mixed linear model parameters.

The book begins by delving into the theory of multivariate distributions, covering identification and testing of covariance structures and means under various multivariate models. It then proceeds to explore estimation in mixed linear models and their transformations.

The results presented in this book originate from the extensive research conducted by the Multivariate and Mixed Linear Models research group during their meetings held at the Mathematical Research and Conference Center in Będlewo, Poland, over the past decade. The book boasts an extensive bibliography of related publications, making it an invaluable resource for Ph.D. students and researchers in modern statistical science who have a keen interest in multivariate and mixed linear models.

Weight: 552g
Dimension: 156 x 234 x 21 (mm)
ISBN-13: 9783030754969
Edition number: 1st ed. 2021