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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Continuous-time statistical models for network panel data

## Continuous-time statistical models for network panel dataAdd to your list(s) Download to your calendar using vCal - Tom Snijders (University of Groningen; University of Oxford)
- Monday 12 December 2016, 09:30-10:30
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact info@newton.ac.uk. SNAW04 - Dynamic networks For the statistical analysis of network panel data even with as little as 2 waves, it is very fruitful to use models that assume a continuous-time Markov network process, observed only at the moments of observation for the panel. This is analogous to the use of continuous-time models for classical (non-network) panel data proposed by Bergstrom, Singer, and others. For network data such an approach was proposed already by Coleman in 1964. The advantage of this approach is that it provides a simple way to represent the feedback that is inherent in network dynamics, and the model can be defined by just specifying the conditional probability of a tie change, given the current state of the network. - Ruth M. Ripley, Tom A.B. Snijders, Zsófia Boda, András Vörös, and Paulina Preciado, 2016.
*Manual for SIENA version 4.0*. Oxford: University of Oxford, Department of Statistics; Nuffield College. http://www.stats.ox.ac.uk/siena/ - Tom A.B. Snijders, 2001. The statistical evaluation of social network dynamics.
*Sociological Methodology*, 31, 361-395. - Tom A.B. Snijders and Johan Koskinen, 2013. “Longitudinal Models”. Chapter 11 (pp. 130-140) in D. Lusher, J. Koskinen, and G. Robins,
*Exponential Random Graph Models for Social Networks*, Cambridge: Cambridge University Press. - T om A.B. Snijders, Gerhard G. van de Bunt, G. G., and Christian E.G. Steglich, 2010. Introduction to actor-based models for network dynamics.
*Social Networks*, 32, 44–60.
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