About this item:

279 Views | 360 Downloads

Author Notes:

Correspondence to: A. James O’Malley, The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA. Ph: 603-653-0854; Fax: 603-653-0896; Email: James.OMalley@Dartmouth.edu

We thank Joel Hoff for expert programming and Nicholas Christakis and Alan Zaslavsky for helpful comments.


Research Funding:

Research for the paper was supported by NIH grant P01 AG031093.


  • Science & Technology
  • Technology
  • Physical Sciences
  • Computer Science, Interdisciplinary Applications
  • Statistics & Probability
  • Computer Science
  • Mathematics
  • Conditional independence
  • Longitudinal
  • Retrospective sampling
  • Social network
  • Sociocentric design
  • Sparse data
  • Weighting
  • TIME

Using retrospective sampling to estimate models of relationship status in large longitudinal social networks


Journal Title:

Computational Statistics and Data Analysis


Volume 82


, Pages 35-46

Type of Work:

Article | Post-print: After Peer Review


Estimation of longitudinal models of relationship status between all pairs of individuals (dyads) in social networks is challenging due to the complex inter-dependencies among observations and lengthy computation times. To reduce the computational burden of model estimation, a method is developed that subsamples the "always-null" dyads in which no relationships develop throughout the period of observation. The informative sampling process is accounted for by weighting the likelihood contributions of the observations by the inverses of the sampling probabilities. This weighted-likelihood estimation method is implemented using Bayesian computation and evaluated in terms of its bias, efficiency, and speed of computation under various settings. Comparisons are also made to a full information likelihood-based procedure that is only feasible to compute when limited follow-up observations are available. Calculations are performed on two real social networks of very different sizes. The easily computed weighted-likelihood procedure closely approximates the corresponding estimates for the full network, even when using low sub-sampling fractions. The fast computation times make the weighted-likelihood approach practical and able to be applied to networks of any size.

Copyright information:

© 2014 Elsevier B.V. All rights reserved.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Creative Commons License

Export to EndNote