ASYMPTOTIC DISTRIBUTION IN DIRECTED FINITE WEIGHTED RANDOM GRAPHS WITH AN INCREASING BI-DEGREE SEQUENCE  

作　　者：Jing LUO Hong QIN Zhenghong WANG 罗敬;覃红;汪政红(Department of Statistics,South-Central University for Nationalities,Wuhan 430074,China;Department of Statistics,Zhongnan University of Economics and Law,Wuhan 430073,China)

机构地区：[1]Department of Statistics,South-Central University for Nationalities,Wuhan 430074,China [2]Department of Statistics,Zhongnan University of Economics and Law,Wuhan 430073,China

出　　处：《Acta Mathematica Scientia》2020年第2期355-368,共14页数学物理学报（B辑英文版）

基　　金：Luo's research is partially supported by the Fundamental Research Funds for the Central Universities(South-Central University for Nationalities(CZQ19010));National Natural Science Foundation of China(11801576);the Scientific Research Funds of South-Central University For Nationalities(YZZ17007);Qin's research is partially supported by National Natural Science Foundation of China(11871237);Wang's research is partially supported by the Fundamental Research Funds for the Central Universities(South-Central University for Nationalities(CZQ18017)).

摘　　要：The asymptotic normality of the fixed number of the maximum likelihood estimators(MLEs)in the directed finite weighted network models with an increasing bi-degree sequence has been established recently.In this article,we further derive the central limit theorem for linear combinations of all the MLEs with an increasing dimension when the edges take finite discrete weight.Simulation studies are provided to illustrate the asymptotic results.

关 键 词：Central limit theorem finite discrete network increasing number of parameters maximum likelihood estimator 

分 类 号：O21[理学—概率论与数理统计]