Kernel Mixtures of Gaussian Processes and their Application to Epidemiology

Richard Maclehose
Postdoctoral Fellow
National Institute of Environmental Health Sciences
*Candidate for the Biostatistics/ Epidemiology Faculty Position

Wednesday, January 30th
3:30pm
Moos 5-125
Minneapolis Campus

Abstract:

In many applications, there is interest in estimating a collection of related functions. For example, in epidemiology these functions may correspond to dose-response curves for an environmental exposure at different lag times or spatial locations. Our focus is on estimating the dose-response effects of chrysotile asbestos exposure during multiple previous times on current mortality. Data come from an occupational cohort study of textile workers in South Carolina. We focus on Bayesian nonparametric methods for incorporating dependence in collections of related functions through an appropriate prior. The standard choice in such settings is a Gaussian process (GP) with a separable covariance function. We propose a more general class of kernel mixtures of Gaussian processes (KMGP), which induces flexible dependence in random functions. Some theoretical properties of the KMGP are formalized. We use the KMGP to develop a class of generalized additive distributed lag models, which are useful in assessing time-varying effects of predictors. Efficient MCMC algorithms are developed for posterior inference. The methods are illustrated using simulations, and an application to the occupational cohort study examining the health effects of asbestos exposure.

A social tea will be held at 3:00 P.M. in A434 Mayo. All are Welcome.
For more details contact 612-624-4655 or see http://www.biostat.umn.edu/seminar_academic.html