Multivariate, Multilinear and Mixed Linear Models

This book explores advanced statistical inference in multivariate and mixed linear models, making it essential for researchers and PhD students.

This book provides an in-depth exploration of the latest findings in statistical inference related to multivariate, multilinear, and mixed linear models. Designed to offer a comprehensive understanding of the subject, Multivariate, Multilinear and Mixed Linear Models includes pioneering contributions from leading experts in the field. The text guides readers through essential topics such as estimation and testing of parameters within these complex models, starting with the foundational theory of multivariate distributions. It also covers the identification and testing of covariance structures and means under various multivariate frameworks, making it a crucial resource for advanced study.

In addition to theoretical insights, the book delves into practical aspects of estimation in mixed linear models and their transformations. The findings presented are the result of collaborative research conducted by the Multivariate and Mixed Linear Models group, which has met regularly at the Mathematical Research and Conference Center in Będlewo, Poland, over the past decade. This collaborative effort has culminated in a collection of carefully curated reviews and research contributions that advance the field of statistical science.

With an extensive bibliography of related publications, Multivariate, Multilinear and Mixed Linear Models is tailored for PhD students and researchers eager to deepen their understanding of modern statistical methodologies. Its thorough approach ensures that readers are well-equipped to navigate the complexities of multivariate and mixed linear models in their own research endeavors.