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Activity Number: 511
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #313761
Title: Inference for Exponential-Family Random Graph Models Based on Egocentrically Sampled Data
Author(s): Pavel N. Krivitsky*+ and Martina Morris
Companies: University of Wollongong and University of Washington
Keywords: social network ; exponential-family random graph model ; egocentric sample ; ERGM ; asymptotics
Abstract:

Egocentric sampling comprises observation of a network of interest from the point of view of a set of sampled actors (egos), who provide information about themselves and their network relations (alters), but who often cannot disambiguate them. It is the only practical way to observe certain classes of networks, such as those of sexual partnerships. Although methods exist for recovering network features from such data, a unifying framework, such as exponential-family random graph (ERG) modeling, is lacking, and, so far, approaches to fitting ERGMs to such data have lacked a rigorous statistical foundation in general and measures of uncertainty in particular. In this work, we identify a subclass of ERGMs amenable to being estimated from such data, develop techniques for doing so, and introduce a technique for rigorously evaluating the uncertainty (i.e., standard errors) of these estimates. For ERGMs parametrized to be invariant to network size, we also describe a computationally tractable approach for fitting these networks. We demonstrate these techniques through a simulation study and apply them to the 1992 National Health and Social Life Survey (NHSLS) data.


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