Testing Goodness of Fit of Random Graph Models

被引：3

作者：

Csiszar, Villo ^{[1
]}

Hussami, Peter ^{[2
]}

Komlos, Janos ^{[3
]}

Mori, Tamas F. ^{[1
]}

Rejto, Lidia ^{[2
,4
]}

Tusnady, Gabor ^{[2
]}

机构：

[1] Eotvos Lorand Univ, Dept Probabil Theory & Stat, H-1053 Budapest, Hungary

[2] Hungarian Acad Sci, Alfred Renyi Math Inst, H-1053 Budapest, Hungary

[3] Rutgers State Univ, Dept Math, New Brunswick, NJ 08901 USA

[4] Univ Delaware, Stat Program, Newark, DE 19716 USA

来源：

ALGORITHMS | 2012年 / 5卷 / 04期

基金：

美国国家科学基金会;

关键词：

random graph; maximum likelihood; rank entropy;

D O I：

10.3390/a5040629

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Random graphs are matrices with independent 0-1 elements with probabilities determined by a small number of parameters. One of the oldest models is the Rasch model where the odds are ratios of positive numbers scaling the rows and columns. Later Persi Diaconis with his coworkers rediscovered the model for symmetric matrices and called the model beta. Here we give goodness-of-fit tests for the model and extend the model to a version of the block model introduced by Holland, Laskey and Leinhard.

引用

页码：629 / 635

页数：7

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