ShopSpell

Linear and Graphical Models for the Multivariate Complex Normal Distribution [Paperback]

$79.99     $109.99    27% Off      (Free Shipping)
100 available
  • Category: Books (Mathematics)
  • Author:  Andersen, Heidi H., Hojbjerre, Malene, Sorensen, Dorte, Eriksen, Poul S.
  • Author:  Andersen, Heidi H., Hojbjerre, Malene, Sorensen, Dorte, Eriksen, Poul S.
  • ISBN-10:  0387945210
  • ISBN-10:  0387945210
  • ISBN-13:  9780387945217
  • ISBN-13:  9780387945217
  • Publisher:  Springer
  • Publisher:  Springer
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Feb-1995
  • Pub Date:  01-Feb-1995
  • SKU:  0387945210-11-SPRI
  • SKU:  0387945210-11-SPRI
  • Item ID: 100821113
  • List Price: $109.99
  • Seller: ShopSpell
  • Ships in: 5 business days
  • Transit time: Up to 5 business days
  • Delivery by: Jul 03 to Jul 05
  • Notes: Brand New Book. Order Now.
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.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.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 CompllS2
Add Review