9 citations · 15 across the 8 of their papers we have counts for
8 papers
Exact Likelihood Inference for Snowball-Sampled Erdős-Rényi Networks
Nurzhan Sapargali, Sergio Buttazzo, G\''oran Kauermann
Network data obtained through link-tracing designs, such as snowball sampling, are collected through a mechanism that depends on the very structure the analysis seeks to estimate.…
Maximum Likelihood Estimation for Network Models with Latent Geometry under Snowball Sampling
Nurzhan Sapargali, Sergio Buttazzo, Göran Kauermann
Snowball sampling is a widely used design for collecting network data from large or hard-to-reach populations, yet naive inference that ignores the sampling mechanism produces syst…
Using LASSO for Variable Selection in Exponential Random Graph models
Sergio Buttazzo, Göran Kauermann
The paper demonstrates the use of LASSO-based estimation in network models. Taking the Exponential Random Graph Model (ERGM) as a flexible and widely used model for network data an…
Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation
Benjamin Sischka, Göran Kauermann
This paper focuses on the comparison of networks on the basis of statistical inference. For that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that…
Stochastic Block Smooth Graphon Model
Benjamin Sischka, Göran Kauermann
The paper proposes the combination of stochastic blockmodels with smooth graphon models. The first allow for partitioning the set of individuals in a network into blocks which repr…
Iterative Estimation of Mixed Exponential Random Graph Models with Nodal Random Effects
Sevag Kevork, Göran Kauermann
The presence of unobserved node specific heterogeneity in Exponential Random Graph Models (ERGM) is a general concern, both with respect to model validity as well as estimation ins…
In Search of Lost Edges: A Case Study on Reconstructing Financial Networks
Michael Lebacher, Samantha Cook, Nadja Klein +1
To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for…
EM-Based Smooth Graphon Estimation Using Bayesian and Spline-Based Approaches
Benjamin Sischka, Göran Kauermann
This paper proposes the estimation of a smooth graphon model for network data analysis using principles of the EM algorithm. The approach considers both variability with respect to…