174 citations · 415 across the 18 of their papers we have counts for
18 papers
Vulnerability Webs: Systemic Risk in Software Networks
Cornelius Fritz, Co-Pierre Georg, Angelo Mele +1
Software development relies on code reuse to minimize costs, creating vulnerability risks through dependencies with substantial economic impact, as seen in the Crowdstrike and Hear…
A latent process model for monitoring progress towards hard-to-measure targets, with applications to mental health and online educational assessments
Minjeong Jeon, Michael Schweinberger
The recent shift to remote learning and work has aggravated long-standing problems, such as the problem of monitoring the mental health of individuals and the progress of students…
A Socio-Demographic Latent Space Approach to Spatial Data When Geography is Important but Not All-Important
Saikat Nandy, Scott H. Holan, Michael Schweinberger
Many models for spatial and spatio-temporal data assume that "near things are more related than distant things," which is known as the first law of geography. While geography may b…
A Continuous-Time Stochastic Process for High-Resolution Network Data in Sports
Nicholas Grieshop, Yong Feng, Guanyu Hu +1
Technological advances have paved the way for collecting high-resolution network data in basketball, football, and other team-based sports. Such data consist of interactions among…
A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015
Michael Schweinberger, Rashmi P. Bomiriya, Sergii Babkin
We consider incomplete observations of stochastic processes governing the spread of infectious diseases through finite populations by way of contact. We propose a flexible semipara…
Pseudo-likelihood-based -estimation of random graphs with dependent edges and parameter vectors of increasing dimension
Jonathan R. Stewart, Michael Schweinberger
An important question in statistical network analysis is how to estimate models of discrete and dependent network data with intractable likelihood functions, without sacrificing co…
Mapping unobserved item-respondent interactions: A latent space item response model with interaction map
Minjeong Jeon, Ick Hoon Jin, Michael Schweinberger +1
Classic item response models assume that all items with the same difficulty have the same response probability among all respondents with the same ability. These assumptions, howev…
Bayesian Model Selection for High-Dimensional Ising Models, With Applications to Educational Data
Jaewoo Park, Ick Hoon Jin, Michael Schweinberger
Doubly-intractable posterior distributions arise in many applications of statistics concerned with discrete and dependent data, including physics, spatial statistics, machine learn…
Multilevel Network Item Response Modeling for Discovering Differences Between Innovation and Regular School Systems in Korea
Ick Hoon Jin, Minjeong Jeon, Michael Schweinberger +2
The innovation school system in South Korea has been developed in response to the traditional high-pressure school system in South Korea, with a view to cultivating a bottom-up and…
Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios
Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts +1
Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework for modeling sparse and dense random graphs, short- and long-tailed degree distributions, co…
A note on the role of projectivity in likelihood-based inference for random graph models
Michael Schweinberger, Pavel N. Krivitsky, Carter T. Butts
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 m…
A two-stage working model strategy for network analysis under Hierarchical Exponential Random Graph Models
Ming Cao
Social networks as a representation of relational data, often possess multiple types of dependency structures at the same time. There could be clustering (beyond homophily) at a ma…
Large-scale estimation of random graph models with local dependence
Sergii Babkin, Jonathan Stewart, Xiaochen Long +1
A class of random graph models is considered, combining features of exponential-family models and latent structure models, with the goal of retaining the strengths of both of them…
Consistent structure estimation of exponential-family random graph models with block structure
Michael Schweinberger
We consider the challenging problem of statistical inference for exponential-family random graph models based on a single observation of a random graph with complex dependence. To…
Concentration and consistency results for canonical and curved exponential-family models of random graphs
Michael Schweinberger, Jonathan Stewart
Statistical inference for exponential-family models of random graphs with dependent edges is challenging. We stress the importance of additional structure and show that additional…
High-Dimensional Multivariate Time Series With Additional Structure
Michael Schweinberger, Sergii Babkin, Katherine Ensor
High-dimensional multivariate time series are challenging due to the dependent and high-dimensional nature of the data, but in many applications there is additional structure that…
Model-based clustering of large networks
Duy Q. Vu, David R. Hunter, Michael Schweinberger
We describe a network clustering framework, based on finite mixture models, that can be applied to discrete-valued networks with hundreds of thousands of nodes and billions of edge…
Maximum likelihood estimation for social network dynamics
Tom A. B. Snijders, Johan Koskinen, Michael Schweinberger
A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuous-time Markov process on the space of all direct…