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.

Inhalt

1 Prerequisites.- 1.1 Complex Matrix Algebra.- 1.2 A Vector Space Isomorphism.- 1.3 Complex Random Variables.- 1.4 Complex Random Vectors and Matrices.- 2 The Multivariate Complex Normal Distribution.- 2.1 The Univariate Complex Normal Distribution.- 2.1.1 The Standard Complex Normal Distribution.- 2.1.2 The Complex Normal Distribution.- 2.2 The Multivariate Complex Normal Distribution.- 2.3 Independence, Marginal and Conditional Distributions.- 2.4 The Multivariate Complex Normal Distribution in Matrix Notation.- 3 The Complex Wishart Distribution and the Complex U-distribution.- 3.1 The Complex Wishart Distribution.- 3.2 The Complex U-distribution.- 4 Multivariate Linear Complex Normal Models.- 4.1 Complex MANOVA Models.- 4.2 Maximum Likelihood Estimation in Complex MANOVA Models.- 4.2.1 Distributions of the Maximum Likelihood Estimators.- 4.3 Hypothesis Testing in Complex MANOVA Models.- 4.3.1 Likelihood Ratio Test Concerning the Mean.- 4.3.2 Likelihood Ratio Test for Independence.- 5 Simple Undirected Graphs.- 6 Conditional Independence and Markov Properties.- 6.1 Conditional Independence.- 6.2 Markov Properties in Relation to Simple Undirected Graphs.- 7 Complex Normal Graphical Models.- 7.1 Notation.- 7.2 The Concentration Matrix.- 7.3 Complex Normal Graphical Models.- 7.4 Maximum Likelihood Estimation of the Concentration Matrix.- 7.4.1 Iterative Proportional Scaling.- 7.5 Decomposition of the Estimation Problem.- 7.5.1 Estimation in Complex Normal Decomposable Models.- 7.6 Hypothesis Testing in Complex Normal Graphical Models.- 7.6.1 Hypothesis Testing in Complex Normal Decomposable Models.- A Complex Matrices.- A.1 Complex Vector Space.- A.2 Basic Operations of Complex Matrices.- A.3 Inverse Matrix.- A.4 Determinant and Eigenvalues.- A.5 Trace and Rank.- A.6 Conjugate Transpose Matrix.- A.7 Hermitian Matrix.- A.8 Unitary Matrix.- A.9 Positive Semidefinite Complex Matrices.- A.10 Positive Definite Complex Matrices.- A.11 Direct Product.- A.12 Partitioned Complex Matrices.- B Orthogonal Projections.- Notation.