The β-model—maximum likelihood, Cramér–Rao bounds, and hypothesis testing
Wahlstrom, J; Skog, I; Rosa, PSL; et al.Handel, P; Nehorai, A
Date: 6 April 2017
Journal
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
We study the maximum-likelihood estimator in a setting where the dependent variable is a random graph and covariates are available on a graph level. The model generalizes the well-known β-model for random graphs by replacing the constant model parameters with regression functions. Cramer-Rao bounds are derived for special cases of the ...
We study the maximum-likelihood estimator in a setting where the dependent variable is a random graph and covariates are available on a graph level. The model generalizes the well-known β-model for random graphs by replacing the constant model parameters with regression functions. Cramer-Rao bounds are derived for special cases of the undirected β-model, the directed β-model, and the covariate-based β-model. The corresponding maximum-likelihood estimators are compared with the bounds by means of simulations. Moreover, examples are given on how to use the presented maximum-likelihood estimators to test for directionality and significance. Finally, the applicability of the model is demonstrated using temporal social network data describing communication among healthcare workers.
Computer Science
Faculty of Environment, Science and Economy
Item views 0
Full item downloads 0