期刊
ANNALS OF APPLIED STATISTICS
卷 4, 期 2, 页码 567-588出版社
INST MATHEMATICAL STATISTICS
DOI: 10.1214/09-AOAS313
关键词
Graphs; longitudinal data; method of moments; stochastic approximation; Robbins-Monro algorithm
资金
- US National Institutes of Health (NIH) [1R01HD052887-01A2]
- Netherlands Organization for Scientific Research (NWO) [401-01-552, 446-06-029]
- EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT [R01HD052887] Funding Source: NIH RePORTER
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.
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