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20172021
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stat.ME2021

Multiple exposure distributed lag models with variable selection

Joseph Antonelli, Ander Wilson, Brent Coull

Distributed lag models are useful in environmental epidemiology as they allow the user to investigate critical windows of exposure, defined as the time period during which exposure…

stat.ME2021

Estimation and false discovery control for the analysis of environmental mixtures

Srijata Samanta, Joseph Antonelli

The analysis of environmental mixtures is of growing importance in environmental epidemiology, and one of the key goals in such analyses is to identify exposures and their interact…

stat.ME2019

Averaging causal estimators in high dimensions

Joseph Antonelli, Matthew Cefalu

There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This…

stat.ME2019

Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models

Ray Bai, Gemma E. Moran, Joseph Antonelli +2

We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse…

stat.ME2018

Causal Inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties

Joseph Antonelli, Georgia Papadogeorgou, Francesca Dominici

We introduce a framework for estimating causal effects of binary and continuous treatments in high dimensions. We show how posterior distributions of treatment and outcome models c…