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Estimation and prediction for stochastic blockstructures

In English

Författare

Summary, in English

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 unobserved (latent) classes and that the probability distribution of the relation between two vertices depends only on the classes to which they belong. A Bayesian estimator based on Gibbs sampling is proposed. The basic model is not identified, because class labels are arbitrary. The resulting identifiability problems are solved by restricting inference to the posterior distributions of invariant functions of the parameters and the vertex class membership. In addition, models are considered where class labels are identified by prior distributions for the class membership of some of the vertices. The model is illustrated by an example from the social networks literature (Kapferer's tailor shop).

Publiceringsår

2001

Språk

Engelska

Sidor

1077-1087

Publikation/Tidskrift/Serie

Journal of the American Statistical Association

Volym

96

Issue

455

Dokumenttyp

Artikel i tidskrift

Förlag

American Statistical Association

Ämne

  • Probability Theory and Statistics

Nyckelord

  • Gibbs sampling
  • social network
  • latent class model
  • mixture model
  • cluster analysis
  • Colored graph

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0162-1459