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Sampling for Conditional Inference on Network Data

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  • Jingfei Zhang
  • Yuguo Chen

Abstract

Random graphs with given vertex degrees have been widely used as a model for many real-world complex networks. However, both statistical inference and analytic study of such networks present great challenges. In this article, we propose a new sequential importance sampling method for sampling networks with a given degree sequence. These samples can be used to approximate closely the null distributions of a number of test statistics involved in such networks and provide an accurate estimate of the total number of networks with given vertex degrees. We study the asymptotic behavior of the proposed algorithm and prove that the importance weight remains bounded as the size of the graph grows. This property guarantees that the proposed sampling algorithm can still work efficiently even for large sparse graphs. We apply our method to a range of examples to demonstrate its efficiency in real problems.

Suggested Citation

  • Jingfei Zhang & Yuguo Chen, 2013. "Sampling for Conditional Inference on Network Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1295-1307, December.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:504:p:1295-1307
    DOI: 10.1080/01621459.2012.758587
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    Cited by:

    1. Zhu, Xuening & Wang, Weining & Wang, Hansheng & Härdle, Wolfgang Karl, 2019. "Network quantile autoregression," Journal of Econometrics, Elsevier, vol. 212(1), pages 345-358.
    2. Pan, Xing & Dang, Yuheng & Wang, Huixiong & Hong, Dongpao & Li, Yuehong & Deng, Hongxu, 2022. "Resilience model and recovery strategy of transportation network based on travel OD-grid analysis," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    3. Bryan S. Graham, 2015. "Methods of Identification in Social Networks," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 465-485, August.

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