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Analysis of Networks with Missing Data with Application to the National Longitudinal Study of Adolescent Health

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SNAW02 - Network Science and its Applications

Co-authors: Krista J. Gile (University of Massachusetts at Amherst), Mark S. Handcock (University of California, Los Angeles)

It is common in the analysis of social network data to assume that it represents a census of the networked population of interest. Often the data result from sampling of the networked population via a known mechanism. However, most social network analysis ignores the problem of missing data by including only actors with complete observations. In this talk we address the modeling of networks with missing data, developing previous ideas in missing data, network modeling, and network sampling. We show the value of the mean value parametrization to study differences between modeling approaches. We also develop goodness-of-fit techniques to better understand model fit. The ideas are motivated by an analysis of a friendship network from the National Longitudinal Study of Adolescent Health. The work presented is by Krista J. Gile and Mark S. Handcock.

This talk is part of the Isaac Newton Institute Seminar Series series.

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