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On the asymptotic normality of estimating the affine preferential attachment network models with random initial degrees

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  • Gao, Fengnan
  • van der Vaart, Aad

Abstract

We consider the estimation of the affine parameter and power-law exponent in the preferential attachment model with random initial degrees. We derive the likelihood, and show that the maximum likelihood estimator (MLE) is asymptotically normal and efficient. We also propose a quasi-maximum-likelihood estimator (QMLE) to overcome the MLE’s dependence on the history of the initial degrees. To demonstrate the power of our idea, we present numerical simulations.

Suggested Citation

  • Gao, Fengnan & van der Vaart, Aad, 2017. "On the asymptotic normality of estimating the affine preferential attachment network models with random initial degrees," Stochastic Processes and their Applications, Elsevier, vol. 127(11), pages 3754-3775.
    • Handle: RePEc:eee:spapps:v:127:y:2017:i:11:p:3754-3775
    DOI: 10.1016/j.spa.2017.03.008
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    Cited by:

    1. Tiandong Wang & Panpan Zhang, 2022. "Directed hybrid random networks mixing preferential attachment with uniform attachment mechanisms," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 957-986, October.
    2. Chan, N.H. & Cheung, Simon K.C. & Wong, Samuel P.S., 2020. "Inference for the degree distributions of preferential attachment networks with zero-degree nodes," Journal of Econometrics, Elsevier, vol. 216(1), pages 220-234.

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