• Publications
  • Influence
Multilevel analysis. An introduction to basic and advanced multilevel modeling, 2nd edition (1st edition 1999).
TLDR
Multilevel theories, multi-stage sampling, and multilevel models, including the random intercept model, and the hierarchical linear model are studied.
Social Network Analysis
  • T. Snijders
  • Computer Science
    International Encyclopedia of Statistical Science
  • 2011
Multilevel Analysis
  • T. Snijders
  • Computer Science
    International Encyclopedia of Statistical Science
  • 2011
The statistical evaluation of social network dynamics
TLDR
A class of statistical models is proposed for longitudinal network data that are continuous-time Markov chain models that can be implemented as simulation models and statistical procedures are proposed that are based on the method of moments.
New Specifications for Exponential Random Graph Models
TLDR
It is concluded that the new specifications of exponential random graph models increase the range and applicability of the ERGM as a tool for the statistical analysis of social networks.
Estimation and Prediction for Stochastic Blockstructures
A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned into several
Markov Chain Monte Carlo Estimation of Exponential Random Graph Models
  • T. Snijders
  • Computer Science, Mathematics
    J. Soc. Struct.
  • 2002
TLDR
This paper is about estimating the parameters of the exponential random graph model using frequentist Markov chain Monte Carlo (MCMC) methods, based on the Robbins-Monro algorithm for approximating a solution to the likelihood equation.
Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure
TLDR
A posteriori blockmodeling for graphs is proposed and it is shown that when the number of vertices tends to infinity while the probabilities remain constant, the block structure can be recovered correctly with probability tending to 1.
...
...