In the last decade, graphical models have become increasingly popular as a statistical tool. This book is the first which provides an account of graphical models for multivariate complex rmal distributions. Beginning with an introduction to the multivariate complex rmal distribution, the authors develop the marginal and conditional distributions of random vectors and matrices. Then they introduce complex MANOVA models and parameter estimation and hypothesis testing for these models. After introducing undirected graphs, they then develop the theory of complex rmal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence.
D. Sorensen, Heidi H. Andersen, Malene Hojbjerre, Poul S. Eriksen