A0773
Title: Statistical inference for subgraph densities under induced random sampling from network data
Authors: Ayoushman Bhattacharya - Washington University in St. Louis (United States) [presenting]
Nilanjan Chakraborty - Missouri University of Science and Technology (United States)
Soumen Lahiri - Washington University in Saint Louis (United States)
Abstract: Statistical inference is considered for subgraph densities of a general population network under without replacement sampling (SRSWOR). Under this sampling scheme, the asymptotic normality of the Horvitz-Thompson (HT) estimator is established for the population subgraph densities under minimal assumptions. The joint asymptotic normality of two subgraph densities is also established, which is crucial in establishing a weak convergence of the global transitivity of the sampled graph. To facilitate the inferential procedures, a jackknife and a bootstrap estimator of the unknown population variance are provided, and their consistency is established. Results find a useful application to the problem of testing the equality of two population graphs using the subgraph densities as the test statistic. Finally, a simulation study and a real data analysis are presented, which corroborate the theoretical findings.
