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

作者机构：Department of StatisticsSouth-Central University for NationalitiesWuhan 430074China Department of StatisticsZhongnan University of Economics and LawWuhan 430073China 

出 版 物：《Acta Mathematica Scientia》 (数学物理学报（B辑英文版）)

年 卷 期：2020年第40卷第2期

页      面：355-368页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

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)) 

主　　题：Central limit theorem finite discrete network increasing number of parameters maximum likelihood estimator 

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 *** 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 *** studies are provided to illustrate the asymptotic results.