A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network.

ABSTRACT: Network models are applied in numerous domains where data arise from systems of interactions among pairs of actors. Both statistical and mechanistic network models are increasingly capable of capturing various dependencies among these actors. Yet, these dependencies pose statistical challenges for analyzing such data, especially when the data set comprises only a single observation of one network, often leading to intractable likelihoods regardless of the modeling paradigm and limiting the application of existing statistical methods for networks. We explore a subsampling bootstrap procedure to serve as the basis for goodness of fit and model selection with a single observed network that circumvents the intractability of such likelihoods. Our approach is based on flexible resampling distrib

SUBMITTER: Chen S 

PROVIDER: S-EPMC6854093 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature