Maximum likelihood estimation for social network dynamics

Statistics – Applications

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Published in at http://dx.doi.org/10.1214/09-AOAS313 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS313

A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuous-time Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The model for tie changes is parametric and designed for applications to social network analysis, where the network dynamics can be interpreted as being generated by choices made by the social actors represented by the nodes of the graph. An algorithm for calculating the Maximum Likelihood estimator is presented, based on data augmentation and stochastic approximation. An application to an evolving friendship network is given and a small simulation study is presented which suggests that for small data sets the Maximum Likelihood estimator is more efficient than the earlier proposed Method of Moments estimator.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Maximum likelihood estimation for social network dynamics does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Maximum likelihood estimation for social network dynamics, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Maximum likelihood estimation for social network dynamics will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-130975

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.