Simulation for Statistical Inference in Dynamic Network Models

Abstract

Actor-oriented models are proposed for the statistical analysis of longitudinal social network data. These models are implemented as simulation models, and the statis- tical evaluation is based on the method of moments and the Robbins-Monro process applied to computer simulation outcomes. In this approach, the calculations that are required for statistical inference are too complex to be carried out analytically, and therefore they are replaced by computer simulation. The statistical models are continuous-time Markov chains. It is shown how the reciprocity model of Wasserman and Leenders can be formulated as a special case of the actor-oriented model.