Finding equilibrium among political,socialand capital powers is the key toChina＇s successTHE Communist Party of China（CPC）draws its strength from the people,who fought alongside it to achieve national independence ...Finding equilibrium among political,socialand capital powers is the key toChina＇s successTHE Communist Party of China（CPC）draws its strength from the people,who fought alongside it to achieve national independence and helped it win the domestic war against the Kuomintang.Nonetheless,the CPC had,at one point,lost its course due to misjudgments and miscalculations,resulting in tragic outcomes such as the＂cultural revolution＂展开更多
In social network analysis, logistic regression models have been widely used to establish the relationship between the response variable and covariates. However, such models often require the network relationships to ...In social network analysis, logistic regression models have been widely used to establish the relationship between the response variable and covariates. However, such models often require the network relationships to be mutually independent, after controlling for a set of covariates. To assess the validity of this assumption,we propose test statistics, under the logistic regression setting, for three important social network drivers. They are, respectively, reciprocity, centrality, and transitivity. The asymptotic distributions of those test statistics are obtained. Extensive simulation studies are also presented to demonstrate their finite sample performance and usefulness.展开更多
文摘Finding equilibrium among political,socialand capital powers is the key toChina＇s successTHE Communist Party of China（CPC）draws its strength from the people,who fought alongside it to achieve national independence and helped it win the domestic war against the Kuomintang.Nonetheless,the CPC had,at one point,lost its course due to misjudgments and miscalculations,resulting in tragic outcomes such as the＂cultural revolution＂
文摘In social network analysis, logistic regression models have been widely used to establish the relationship between the response variable and covariates. However, such models often require the network relationships to be mutually independent, after controlling for a set of covariates. To assess the validity of this assumption,we propose test statistics, under the logistic regression setting, for three important social network drivers. They are, respectively, reciprocity, centrality, and transitivity. The asymptotic distributions of those test statistics are obtained. Extensive simulation studies are also presented to demonstrate their finite sample performance and usefulness.
