This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 66
Type: Topic Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Health Policy Statistics Section
Abstract - #309341
Title: Modeling Networks When Data Are Missing or Sampled
Author(s): Mark Stephen Handcock*+ and Krista J. Gile
Companies: University of California, Los Angeles and Nuffield College
Address: Department of Statistics, Los Angeles, CA, 90095-1554, USA
Keywords: exponential families ; social network ; network sampling ; likelihood

Network models are widely used to represent relational information among interacting units and the structural implications of these relations.

Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition even if a census of a population is attempted, individuals and links between individuals are missed.

We develop the conceptual and computational theory for inference based on sampled network information. We consider inference within the likelihood framework based on both conventional and adaptive network designs.

We illustrate these ideas via link-tracing sampling designs on a collaboration network.

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