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

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

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

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.

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

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

(2023).
Modeling the Visibility Distribution for Respondent-Driven Sampling with Application to Population Size Estimation.
To appear in the Annals of Applied Statistics.

(2023).
Understanding Networks with Exponential-family Random Network Models.
Social Networks.

(2023).
Comparative assessment of alternative methods for evaluating the optimality of ground motion intensity measures using woodframe buildings.
Soil Dynamics and Earthquake Engineering.

(2023).
(2023).
A Practical Revealed Preference Model for Separating Preferences and Availability Effects in Marriage Formation.
Journal of the Royal Statistical Society, A.

(2023).
Practical Network Modeling via Tapered Exponential-family Random Graph Models.
Journal of Computational and Graphical Statistics.

(2022).
Tool for tracking all-cause mortality and estimating excess mortality to support the COVID-19 pandemic response.
Western Pacific Surveillance and Response Journal.

(2022).
A New Record Minimum for Antarctic Sea Ice.
Nature Reviews Earth & Environment.

(2022).
Comparing the real-world performance of exponential-family random graph models and latent order logistic models for social network analysis.
Journal of the Royal Statistical Society, A.

(2022).
The promise of upward mobility—the notion that everyone has the chance to get ahead—is one of this country’s most cherished …

In social science research, differences among groups or changes over time are a common focus of study. While means and variances are …

The most effective way to learn statistics is by actively engaging in doing the statistical analysis. This idea drives this casebook. …

Most inference using social network models assumes that the presence or absence of all relations is known. This is rarely the case. …

In this paper, we illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate …

In many situations information from a sample of individuals can be supplemented by population level information on the relationship …

Network models are widely used to represent relations among interacting units or actors. Network data often exhibit transitivity, …

Network data arise in a wide variety of applications. Although descriptive statistics for networks abound in the literature, the …

We present a systematic examination of real network datasets using maximum likelihood estimation for exponential random graph models as …

The most promising class of statistical models for expressing structural properties of social networks is the class of Exponential …

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent …

This paper presents recent advances in the statistical modeling of random graphs that have an impact on the empirical study of social …

Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate, or limited …

Recent work has focused attention on statistical inference for the population distribution of the number of sexual partners based on …

Epidemic thresholds in network models of heterogeneous populations characterized by highly right-skewed contact distributions can be …

Sexually-Transmitted Diseases (STDs) constitute a major public health concern. Mathematical models for the transmission dynamics of …

Relative distribution methods are a nonparametric statistical approach to the comparison of distributions. These methods combine the …

Recent research into the properties of human sexual contact networks has suggested that the degree distribution of the contact graph …

There has been a growing interest in the application of social network theory to the epidemiology of sexually-transmitted diseases …