no code implementations • 15 Apr 2022 • Wenying Deng, Beau Coker, Rajarshi Mukherjee, Jeremiah Zhe Liu, Brent A. Coull
We develop a simple and unified framework for nonlinear variable selection that incorporates uncertainty in the prediction function and is compatible with a wide range of machine learning models (e. g., tree ensembles, kernel methods, neural networks, etc).
1 code implementation • 12 Jun 2020 • Yanelli Nunez, Elizabeth A. Gibson, Eva M. Tanner, Chris Gennings, Brent A. Coull, Jeff A. Goldsmith, Marianthi-Anna Kioumourtzoglou
Statistical learning (SL) includes methods that extract knowledge from complex data.
Applications
2 code implementations • 13 Dec 2019 • Michele Zemplenyi, Mark J. Meyer, Andres Cardenas, Marie-France Hivert, Sheryl L. Rifas-Shiman, Heike Gibson, Itai Kloog, Joel Schwartz, Emily Oken, Dawn L. DeMeo, Diane R. Gold, Brent A. Coull
The ability to identify time periods when individuals are most susceptible to exposures, as well as the biological mechanisms through which these exposures act, is of great public health interest.
Applications Methodology
no code implementations • 8 Dec 2018 • Jeremiah Zhe Liu, John Paisley, Marianthi-Anna Kioumourtzoglou, Brent A. Coull
Ensemble learning is a mainstay in modern data science practice.
1 code implementation • 16 Nov 2018 • Katrina L. Devick, Linda Valeri, Jarvis Chen, Alejandro Jara, Marie-Abèle Bind, Brent A. Coull
The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease.
Applications Methodology
1 code implementation • 30 Nov 2017 • Joseph Antonelli, Maitreyi Mazumdar, David Bellinger, David C. Christiani, Robert Wright, Brent A. Coull
To estimate the health effects of complex mixtures we propose a flexible Bayesian approach that allows exposures to interact with each other and have nonlinear relationships with the outcome.
Methodology