Saved in:
Bibliographic Details
Main Authors: Krivitsky, Pavel N., Kuvelkar, Alina R., Hunter, David R.
Format: Preprint
Published: 2022
Subjects:
Online Access:https://arxiv.org/abs/2202.03572
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911765552431104
author Krivitsky, Pavel N.
Kuvelkar, Alina R.
Hunter, David R.
author_facet Krivitsky, Pavel N.
Kuvelkar, Alina R.
Hunter, David R.
contents This article discusses the problem of determining whether a given point, or set of points, lies within the convex hull of another set of points in $d$ dimensions. This problem arises naturally in a statistical context when using a particular approximation to the loglikelihood function for an exponential family model; in particular, we discuss the application to network models here. While the convex hull question may be solved via a simple linear program, this approach is not well known in the statistical literature. Furthermore, this article details several substantial improvements to the convex hull-testing algorithm currently implemented in the widely used 'ergm' package for network modeling.
format Preprint
id arxiv_https___arxiv_org_abs_2202_03572
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming
Krivitsky, Pavel N.
Kuvelkar, Alina R.
Hunter, David R.
Computation
This article discusses the problem of determining whether a given point, or set of points, lies within the convex hull of another set of points in $d$ dimensions. This problem arises naturally in a statistical context when using a particular approximation to the loglikelihood function for an exponential family model; in particular, we discuss the application to network models here. While the convex hull question may be solved via a simple linear program, this approach is not well known in the statistical literature. Furthermore, this article details several substantial improvements to the convex hull-testing algorithm currently implemented in the widely used 'ergm' package for network modeling.
title Likelihood-based Inference for Exponential-Family Random Graph Models via Linear Programming
topic Computation
url https://arxiv.org/abs/2202.03572