The $\beta$-Model—Maximum Likelihood, Cramér–Rao Bounds, and Hypothesis Testing
Johan Wahlström, Isaac Skog, Patricio Salvatore La Rosa, Peter Händel, Arye Nehorai · IEEE Transactions on Signal Processing · 2017
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.