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June 2010 Maximum likelihood estimation for social network dynamics
Tom A. B. Snijders, Johan Koskinen, Michael Schweinberger
Ann. Appl. Stat. 4(2): 567-588 (June 2010). DOI: 10.1214/09-AOAS313

Abstract

A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuous-time Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The model for tie changes is parametric and designed for applications to social network analysis, where the network dynamics can be interpreted as being generated by choices made by the social actors represented by the nodes of the graph. An algorithm for calculating the Maximum Likelihood estimator is presented, based on data augmentation and stochastic approximation. An application to an evolving friendship network is given and a small simulation study is presented which suggests that for small data sets the Maximum Likelihood estimator is more efficient than the earlier proposed Method of Moments estimator.

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Tom A. B. Snijders. Johan Koskinen. Michael Schweinberger. "Maximum likelihood estimation for social network dynamics." Ann. Appl. Stat. 4 (2) 567 - 588, June 2010. https://doi.org/10.1214/09-AOAS313

Information

Published: June 2010
First available in Project Euclid: 3 August 2010

zbMATH: 1194.62132
MathSciNet: MR2758640
Digital Object Identifier: 10.1214/09-AOAS313

Keywords: Graphs , longitudinal data , method of moments , Robbins–Monro algorithm , stochastic approximation

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 2 • June 2010
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