Linear and Graphical Models : for the Multivariate Complex Normal Distribution /
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...
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| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York,
1995.
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| Series: | Lecture notes in statistics (Springer-Verlag) ;
101. |
| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Item Description: | Electronic resource. |
| Physical Description: | 1 online resource (183 pages) |
| ISBN: | 9781461242406 (electronic bk.) 1461242401 (electronic bk.) |