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Journal of the Royal Statistical Society. Series C, Applied Statistics|May 7, 2026
Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approachSaskia Comess, Daniel E Ho, Joshua L Warren
Biostatistics (Oxford, England)|August 19, 2022
A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirthSaskia Comess, Howard H Chang, Joshua L Warren
Scientific Data|November 19, 2025
A Comprehensive Dataset of Factory Farms in California Compiled Using Computer Vision and Human ValidationVarun Magesh, Nicolas Rothbacher, Saskia Comess, et al.
Environmental Pollution (Barking, Essex : 1987)|May 24, 2021
Exposure to atmospheric metals using moss bioindicators and neonatal health outcomes in Portland, OregonSaskia Comess, Geoffrey Donovan, Demetrios Gatziolis, et al.
Frontiers in Artificial Intelligence|November 13, 2020
Bringing Big Data to Bear in Environmental Public Health: Challenges and RecommendationsSaskia Comess, Alexia Akbay, Melpomene Vasiliou, et al.
Epidemiology (Cambridge, Mass.)|January 9, 2020
Estimating Serotype-specific Efficacy of Pneumococcal Conjugate Vaccines Using Hierarchical ModelsJoshua L Warren, Daniel M Weinberger
Gates Open Research|October 30, 2020
Estimating the power to detect a change caused by a vaccine from time series dataDaniel M Weinberger, Joshua L Warren
Journal of the Royal Statistical Society. Series A, (Statistics in Society)|June 12, 2018
Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approachJoshua L Warren, Penny Gordon-Larsen
Statistical Applications in Genetics and Molecular Biology|March 27, 2017
A Bayesian semiparametric factor analysis model for subtype identificationJiehuan Sun, Joshua L Warren, Hongyu Zhao
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