Linear and Graphical Models: for the Multivariate Complex Normal Distribution: 101 (Lecture Notes in Statistics, 101) - Image 1

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Springer Linear and Graphical Models: for the Multivariate Complex Normal Distribution: 101 (Lecture Notes in Statistics, 101)

Name: Linear and Graphical Models: for the Multivariate Complex Normal Distribution: 101 (Lecture Notes in Statistics, 101)
Brand: Springer

Applied Mathematics

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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 Specifications

Brand: Springer
Format: paperback
ASIN: 0387945210
Category: Books > Subjects > Science, Nature & Maths > Mathematics > Applied Mathematics
Domain: Amazon UK
Publication Date: 19 May 1995
Listed Since: 16 December 2006

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Springer Linear and Graphical Models: for the Multivariate Complex Normal Distribution: 101 (Lecture Notes in Statistics, 101)

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