ENGLISH

Linear and Graphical Models: for the Multivariate Complex Normal Distribution

By D. Sorensen , H. H. Andersen , M. Højbjerre , P. S. Eriksen (auth.)

Book information

Publisher
Springer-Verlag New York
Year
1995
ISBN
978-0-387-94521-7, 978-1-4612-4240-6
DOI
10.1007/978-1-4612-4240-6
Language
english
Format
PDF
Filesize
3 MB (3495117 bytes)
Series
Lecture Notes in Statistics 101
Edition
1
Pages
183\187
Orientation
yes
Scanned
yes
Time added
2013-08-01 04:00:00
Probability Theory and Stochastic Processes

Description

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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