JSM 2005 - Toronto

Abstract #302543

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Activity Number: 382
Type: Invited
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #302543
Title: Latent Variable Semiparametric Regression Models for Spatial-temporal Modeling of Mobile Source Pollution in the Greater Boston Area
Author(s): Brent Coull*+ and Alexandros Gryparis and Joel Schwartz and Helen Suh
Companies: Harvard School of Public Health and Harvard School of Public Health and Harvard School of Public Health and Harvard School of Public Health
Address: Department of Biostatistics, HSPH, Boston, MA, 02115,
Keywords: generalized additive model ; kriging ; particulate matter ; penalized spline
Abstract:

In this paper, we consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon concentrations in the greater Boston area. Measurements of this pollutant, which are a marker of traffic pollution, were obtained from several individual exposure studies conducted at specific household locations, as well as 15 ambient monitoring sites in city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial variability in exposure. In addition, the individual exposure studies recorded different forms of black carbon, with some recording only outdoor concentrations, some recording indoor concentrations, and others recording both. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalized spline formulation of the model that leads to a natural Bayesian Markov chain Monte Carlo algorithm for model fitting and present results from an analysis that applies the model to data from summer and winter separately.

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Revised March 2005