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Statistical Modeling of Networked Evolutionary Public Goods Games

Repeated small dynamic networks are integral to studies in evolutionary game theory, where networked public goods games offer novel insights into human behaviors. Building on these findings, it is necessary to develop a statistical model that …

A Twenty-First Century Structural Change in Antarctica’s Sea Ice System

From 1979 to 2016, total Antarctic sea ice extent experienced a positive trend with record winter maxima in 2012 and 2014. Record summer minima followed within the period 2017-2024, raising the possibility that the Antarctic sea ice system might be …

A Bayesian Model for 20th Century Antarctic Sea Ice Extent Reconstruction

Antarctic sea ice, a key component in the complex Antarctic climate system, is an important driver and indicator of the global climate. In the relatively short satellite-observed period from 1979 to 2022 the sea ice extent has continuously increased …

Estimating Asymptomatic and Symptomatic Transmission of the COVID-19 First Few Cases in Selenge Province, Mongolia

In this paper we undertake a scientific study of a COVID-19 outbreak in Sukhbaatar, Mongolia. We do so by analyzing data collected via contact tracing of cases by the rapid response team. We use a novel statistical model of the transmission networks …

Modeling the Visibility Distribution for Respondent-Driven Sampling with Application to Population Size Estimation

We develop a method for improved estimation of a participant’s inclusion probability based on their network size (degree) as well as other information.

Causal Inference over Stochastic Networks

Claiming causal inferences in network settings necessitates careful consideration of the often complex dependency between outcomes for actors. Of particular importance are treatment spillover or outcome interference effects. We consider causal …

Exposure Notification System activity as a leading indicator for SARS-COV-2 caseload forecasting

We show that smartphone based Exposure Notification systems can significantly improve the accuracy of short-term forecasting of COVID-19 caseloads.

Understanding Networks with Exponential-family Random Network Models

We argue that a new class of exponential-family models for networks is more appriopriate when some of the covariates are stochastic.

Comparative assessment of alternative methods for evaluating the optimality of ground motion intensity measures using woodframe buildings

Probabilistic performance-based earthquake engineering frameworks depend on the choice of an optimal ground motion intensity measure. We evaluate this choice based on two widely known evaluation metrics: efficiency and sufficiency.

Modeling of networked populations when data is sampled or missing

We discuss network modeling with a novel exponential-family class of models when the network has some stochastic covariates and is only partially observed.