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

被引：11
|
作者：
Gao, Fengnan [1 ,2 ]
van der Vaart, Aad [3 ]
机构：
[1] Fudan Univ, Sch Data Sci, Handan Rd 220, Shanghai 200433, Peoples R China
[2] Shanghai Ctr Math Sci, Handan Rd 220, Shanghai 200433, Peoples R China
[3] Leiden Univ, Math Inst, POB 9512, NL-2300 RA Leiden, Netherlands
来源：
STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 2017年 / 127卷 / 11期
基金：
欧洲研究理事会;
关键词：
Preferential attachment model; Complex networks; Statistical inference; Asymptotic normality;
D O I：
10.1016/j.spa.2017.03.008
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：3754 / 3775
页数：22
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