Race and Superfund site remediation.

Superfund is a federal program established in 1980 to manage the cleanup of hazardous waste sites across the United States. Given the health and economic costs borne by people living near these sites, any demographic disparities within the Superfund program are issues of environmental justice. We investigate whether racial demographics local to a Superfund site are associated with its cleanup status, and if so, how.

Variable Selection for High-dimensional Nodal Attributes in Social Networks with Degree Heterogeneity.

We consider a class of network models, in which the connection probability depends on ultrahigh-dimensional nodal covariates () and node-specific popularity (). A Bayesian method is proposed to select nodal features in both dense and sparse networks under a mild assumption on popularity parameters. The proposed approach is implemented via Gibbs sampling.

Estimation and inference for the mediation effect in a time-varying mediation model.

Background: Traditional mediation analysis typically examines the relations among an intervention, a time-invariant mediator, and a time-invariant outcome variable. Although there may be a total effect of the intervention on the outcome, there is a need to understand the process by which the intervention affects the outcome (i.e.

Variable selection for partially linear models via Bayesian subset modeling with diffusing prior.

Most existing methods of variable selection in partially linear models (PLM) with ultrahigh dimensional covariates are based on partial residuals, which involve a two-step estimation procedure. While the estimation error produced in the first step may have an impact on the second step, multicollinearity among predictors adds additional challenges in the model selection procedure. In this paper, we propose a new Bayesian variable selection approach for PLM.

Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure.

Background: Causal effect estimation with observational data is subject to bias due to confounding, which is often controlled for using propensity scores. One unresolved issue in propensity score estimation is how to handle missing values in covariates.

Method: Several approaches have been proposed for handling covariate missingness, including multiple imputation (MI), multiple imputation with missingness pattern (MIMP), and treatment mean imputation.

Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data.

Background: Ambulatory assessment of electrodermal activity (EDA) is an emerging technique for capturing individuals' autonomic responses to real-life events. There is currently little guidance available for processing and analyzing such data in an ambulatory setting.

Objective: This study aimed to describe and implement several methods for preprocessing and constructing features for use in modeling ambulatory EDA data, particularly for measuring stress.

Automated Proposition Density Analysis for Discourse in Aphasia.

Purpose: This study evaluates how proposition density can differentiate between persons with aphasia (PWA) and individuals in a control group, as well as among subtypes of aphasia, on the basis of procedural discourse and personal narratives collected from large samples of participants.

Method: Participants were 195 PWA and 168 individuals in a control group from the AphasiaBank database. PWA represented 6 aphasia types on the basis of the Western Aphasia Battery-Revised (Kertesz, 2006).