Presents the latest results on inference in multivariate, multilinear and mixed linear models

Features review contributions by experts in the field

Provides an extensive bibliography of related publications

Preface.- Holonomic gradient method for multivariate distribution theory (Akimichi Takemura).- From normality to skewed multivariate distributions: a personal view (Tõnu Kollo).- Multivariate moments in multivariate analysis (Jolanta Pielaszkiewicz and Dietrich von Rosen).- Regularized estimation of covariance structure through quadratic loss function (Defei Zhang, Xiangzhao Cui, Chun Li, Jine Zhao, Li Zeng, and Jianxin Pan).- Separable covariance structure identification for doubly multivariate data (Katarzyna Filipiak, Daniel Klein, and Monika Mokrzycka).- Estimation and testing of the covariance structure of doubly multivariate data (Katarzyna Filipiak and Daniel Klein).- Testing equality of mean vectors with block-circular and block compound-symmetric covariance matrices (Carlos A. Coelho).- Estimation and testing hypotheses in two-level and three-level multivariate data with block compound symmetric covariance structure (Arkadiusz Koziö, Anuradha Roy, Roman Zmy¿lony, Ivan Žežula, and Miguel Fonseca).- Testing of multivariate repeated measures data with block exchangeable covariance structure (Ivan Žežula, Daniel Klein, and Anuradha Roy).- On a simplified approach to estimation in experiments with orthogonal block structure (Rados¿aw Kala).- A review of the linear sufficiency and linear prediction sufficiency in the linear model with new observations (Stephen J. Haslett, Jarkko Isotalo, Rados¿aw Kala, Augustyn Markiewicz, and Simo Puntanen).- Linear mixed-effects model using penalized spline based on data transformation methods (Syed Ejaz Ahmed, Dursun Ayd¿n and Ersin Y¿lmaz).- MMLM meetings - List of Publications.- Index.