Andersen H.H., Højbjerre M., Sørensen D., Eriksen P.S. Linear and Graphical Models: for the Multivariate Complex Normal Distribution

New York: Springer, 1995. — 187 p.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 normal distributions. Beginning with an introduction to the multivariate complex normal 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 normal graphical models including the maximum likelihood estimation of the concentration matrix and hypothesis testing of conditional independence.Prerequisites
The Multivariate Complex Normal Distribution
The Complex Wishart Distribution and the Complex U -distribution
Multivariate Linear Complex Normal Models
Simple Undirected Graphs
Conditional Independence and Markov Properties
Complex Normal Graphical Models