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MCMC estimation for the p(2) network regression model with crossed random effects.

Abstract:

The p(2) model is a statistical model for the analysis of binary relational data with covariates, as occur in social network studies. It can be characterized as a multinomial regression model with crossed random effects that reflect actor heterogeneity and dependence between the ties from and to the same actor in the network. Three Markov chain Monte Carlo (MCMC) estimation methods for the p(2) model are presented to improve iterative generalized least squares (IGLS) estimation developed earl...

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Publication status:
Published

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Publisher copy:
10.1348/000711007x255336

Authors


Zijlstra, BJ More by this author
van Duijn, MA More by this author
Snijders, TA More by this author
Journal:
The British journal of mathematical and statistical psychology
Volume:
62
Issue:
Pt 1
Pages:
143-166
Publication date:
2009-02-05
DOI:
EISSN:
2044-8317
ISSN:
0007-1102
URN:
uuid:0695c70e-8ff8-404b-9fee-86fdc26b8f9e
Source identifiers:
97833
Local pid:
pubs:97833

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