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

Fengnan Gao and Aad van der Vaart

Stochastic Processes and their Applications, 2017, vol. 127, issue 11, 3754-3775

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.

Keywords: Preferential attachment model; Complex networks; Statistical inference; Asymptotic normality (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:spapps:v:127:y:2017:i:11:p:3754-3775

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DOI: 10.1016/j.spa.2017.03.008

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