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Inference in a probit model for affiliation networks

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  • Qian Wang
  • Chen Zhao
  • Jing Luo

Abstract

Affiliation network is a two-mode social network composed of two different groups of nodes (a group of actors and a group of social events) and the edge between nodes to represent the affiliation relationship between actors and social events. Although a number of statistical models are proposed to analyze affiliation networks, the probit distribution to model the degree heterogeneity of the affiliation networks are still unknown or have not been properly explored. In this paper, we use the probit distribution to model the degree heterogeneity of the affiliation networks and establish the uniform consistency and the asymptotic normality of the moment estimator when the number of actors and events both go to infinity. Simulation studies and a real data example demonstrate our theoretical results.

Suggested Citation

  • Qian Wang & Chen Zhao & Jing Luo, 2022. "Inference in a probit model for affiliation networks," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(21), pages 7528-7546, November.
    • Handle: RePEc:taf:lstaxx:v:51:y:2022:i:21:p:7528-7546
    DOI: 10.1080/03610926.2021.1873381
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