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A note on asymptotic distributions in maximum entropy models for networks

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  • Yan, Ting

Abstract

A central limit theorem for a linear combination of all the maximum likelihood estimators with an increasing dimension in maximum entropy models for network data, has been established. Simulation studies illustrate the asymptotic results.

Suggested Citation

  • Yan, Ting, 2015. "A note on asymptotic distributions in maximum entropy models for networks," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 1-5.
    • Handle: RePEc:eee:stapro:v:98:y:2015:i:c:p:1-5
    DOI: 10.1016/j.spl.2014.12.003
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    References listed on IDEAS

    as
    1. Yan, Ting & Zhao, Yunpeng & Qin, Hong, 2015. "Asymptotic normality in the maximum entropy models on graphs with an increasing number of parameters," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 61-76.
    2. Ting Yan & Jinfeng Xu, 2013. "A central limit theorem in the β-model for undirected random graphs with a diverging number of vertices," Biometrika, Biometrika Trust, vol. 100(2), pages 519-524.
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    Citations

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    Cited by:

    1. Yong, Zhang & Chen, Siyu & Qin, Hong & Yan, Ting, 2016. "Directed weighted random graphs with an increasing bi-degree sequence," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 235-240.
    2. Su, Liju & Qian, Xiaodi & Yan, Ting, 2018. "A note on a network model with degree heterogeneity and homophily," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 27-30.

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