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Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics)
Andersen, Heidi H., Hojbjerre, Malene, Sorensen, Dorte, Eriksen, Poul S.
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Description for Linear and Graphical Models: for the Multivariate Complex Normal Distribution (Lecture Notes in Statistics)
Paperback. 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 hypothesis testing for these models. Series: Lecture Notes in Statistics. Num Pages: 183 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 233 x 155 x 14. Weight in Grams: 294.
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.
Product Details
Format
Paperback
Publication date
1995
Publisher
Springer
Condition
New
Series
Lecture Notes in Statistics
Number of Pages
183
Place of Publication
New York, NY, United States
ISBN
9780387945217
SKU
V9780387945217
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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