Linear and Graphical Models: for the Multivariate Complex Normal Distribution

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Springer Science & Business Media, May 19, 1995 - Mathematics - 183 pages
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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Contents

I
1
II
3
III
4
IV
7
V
15
VII
18
VIII
22
IX
27
XXVIII
118
XXIX
121
XXX
129
XXXI
137
XXXII
145
XXXIII
147
XXXIV
159
XXXV
163

X
32
XI
39
XII
40
XIII
55
XIV
67
XVI
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XVII
73
XVIII
75
XX
80
XXI
85
XXII
99
XXIV
106
XXV
115
XXVI
116
XXXVI
165
XXXVIII
166
XXXIX
167
XLI
168
XLII
169
XLIII
170
XLV
171
XLVII
172
XLVIII
173
XLIX
175
L
177
LI
181
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