Estimating the Effects of Fine Particulate Matter on 432 Cardiovascular Diseases Using Multi-Outcome Regression With Tree-Structured Shrinkage

DOI10.1080/01621459.2020.1722134zbMath1452.62852OpenAlexW3007935674MaRDI QIDQ5146019

Lorenzo Trippa, Emma G. Thomas, Francesca Dominici, Giovanni Parmigiani

Publication date: 22 January 2021

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2020.1722134

zbMATH Keywords

air pollutioncardiovascular diseasemulti-response regressionBayesian sparsity\(PM_{2.5}\)

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Uses Software

• collapsibleTree
• PRMLT

Cites Work

• Unnamed Item
• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• The solution path of the generalized lasso
• Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping
• PRMLT
• Optimal predictive model selection.
• An introduction to variational methods for graphical models
• Spike and slab variable selection: frequentist and Bayesian strategies
• Causal inference in the context of an error prone exposure: air pollution and mortality
• Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies
• Estimating the acute health effects of coarse particulate matter accounting for exposure measurement error
• The horseshoe estimator for sparse signals
• The Bayesian Lasso
• A measurement error model for time-series studies of air pollution and mortality
• 10.1162/jmlr.2003.3.4-5.993
• Sparsity and Smoothness Via the Fused Lasso
• Bayesian Models for Multiple Outcomes Nested in Domains