A note on the role of projectivity in likelihood-based inference for random graph models
Statistics Theory
2017-07-04 v1 Statistics Theory
Abstract
There is widespread confusion about the role of projectivity in likelihood-based inference for random graph models. The confusion is rooted in claims that projectivity, a form of marginalizability, may be necessary for likelihood-based inference and consistency of maximum likelihood estimators. We show that likelihood-based superpopulation inference is not affected by lack of projectivity and that projectivity is not a necessary condition for consistency of maximum likelihood estimators.
Keywords
Cite
@article{arxiv.1707.00211,
title = {A note on the role of projectivity in likelihood-based inference for random graph models},
author = {Michael Schweinberger and Pavel N. Krivitsky and Carter T. Butts},
journal= {arXiv preprint arXiv:1707.00211},
year = {2017}
}