Linear and Graphical Models : for the Multivariate Complex Normal Distribution

Heidi H. Andersen

ISBN 10: 0387945210 / ISBN 13: 9780387945217

Published by Springer, Springer, 1995

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9780387945217

Bibliographic Details

• Title: Linear and Graphical Models : for the Multivariate Complex Normal Distribution
• Publisher: Springer, Springer
• Publication Date: 1995
• Language: English
• ISBN 10: 0387945210
• ISBN 13: 9780387945217
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 12D millimeters 235 millimeters width by 155 millimeters height by 12 millimeters depth
• Weight: 312 grams

About this title

Synopsis

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.