2026 Colloquia

Flexible Models for Multi-pollutant Mixtures and Health

Glen McGee, PhD
University of Waterloo

An important goal of environmental health research is to assess the risks posed by mixtures of multiple environmental exposures. In these mixtures analyses, flexible models like Bayesian kernel machine regression and multiple index models are appealing because they allow for arbitrary non-linear exposure-outcome relationships. However, this flexibility comes at the cost of low power, particularly when exposures are highly correlated and the health effects are weak. The first part of this talk will discuss a multivariate index modelling strategy that borrows strength across exposures and outcomes by exploiting similar mixture component weights and exposure-response relationships. We then extend the proposed approach to the multiple index model setting where the true index structure is unknown. The second part of the talk will focus on distributed lag non-linear models (DLNMs) for time series analyses of mortality and a mixture of multiple air pollutants, in which the timing of exposure effects is unknown. We propose a unified framework for multiple exposure DLNMs, integrating model specification, estimation, selection and stacking. The framework applies to four different model structures: two additive and two proposed single-index DLNMs for general outcome types. We develop a common estimation approach that applies to all four models, and we extend a model stacking approach to combine inferences across the four DLNMs.

Thursday, April 23, 2026
3:30 p.m. - 5:00 p.m.
Helen Wood Hall, Room 1W-501

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Model-assisted Reinforcement Learning

Eric Laber, PhD
Duke University

Reinforcement learning approaches are categorized as model-based or model-free by their dependence on a system dynamics model. We propose a model-assisted estimator that combines the advantages of model-based estimators with the model-free framework of V-learning. We show that this estimator is doubly-robust, being consistent when either (but not necessarily both) a model-free estimator or a model-based estimator is correctly specified. Furthermore, it is efficient when both estimators are correctly specified. The model-assisted estimator exhibits considerable improvement over model-based and model-free estimators in experiments.
Thursday, April 16, 2026

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Monte Carlo Inference for Semiparametric Bayesian Regression

Daniel Kowal, PhD
Cornell University

Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. We introduce a simple, general, and efficient strategy for joint posterior inference of an unknown transformation and all regression model parameters. The proposed approach delivers (1) joint posterior consistency under general conditions, including multiple model misspecifications, and (2) efficient Monte Carlo (not MCMC) sampling for the transformation and all parameters for important special cases. We illustrate the methodology for simulated and real data analysis with semiparametric Bayesian linear models, quantile regression, and Gaussian processes. These tools apply across a variety of data domains, including real-valued, positive, compactly-supported, and discrete (e.g., count) data.
Thursday, March 12, 2026