A1592
Title: Bootstrapping network statistics using overlapping partitions
Authors: Sayan Chakrabarty - University of Michigan (United States) [presenting]
Liza Levina - University of Michigan (United States)
Abstract: Bootstrapping network data efficiently is a challenging task. Existing methods tend to make strong assumptions on both the network structure and the statistics being bootstrapped, and are computationally costly. A general algorithm, OPBoot, is introduced for network bootstrap that partitions the network into multiple overlapping subnetworks and then aggregates results from bootstrapping these subnetworks to generate a bootstrap sample of the network statistic of interest. This approach is usually much faster than competing methods as most of the computations are done on smaller subnetworks. OPBoot is shown to be consistent in distribution for a large class of network statistics under minimal assumptions on the network structure, and extensive numerical examples demonstrate that the bootstrap confidence intervals produced by OPBoot attain good coverage without substantially increasing interval lengths in a fraction of the time needed for running competing methods.
