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.
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