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Activity Number: 636 - Statistical Methods of Air Quality and Exposure
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #329185 Presentation
Title: Probabilistic Predictive Principal Component Analysis for Spatially-Misaligned and High-Dimensional Air Pollution Data with Missing Observations
Author(s): Phuong T Vu* and Adam A Szpiro
Companies: University of Washington and University of Washington
Keywords: air pollution; environmental epidemiology; spatial misalignment; high-dimensional; missing data; dimension reduction

Environmental studies often focus on the health impacts of long-term air pollution exposure on human subjects. Pollutant concentrations are measured at regulatory monitoring locations, which are usually located at locations different than the study subjects. This spatial misalignment motivates a two-stage modeling approach with an exposure model and a health regression model. In addition, air pollution is often a mixture of many components with different health implications. Conventional approaches incorporate techniques such as principal component analysis (PCA) to obtain a lower-dimensional representation of the data. Recently developed predictive PCA modifies the optimization criterion to improve the exposure model. However, these approaches require complete data. Real-world data tend to have complex missing patterns, including some pollutants that are measured at relatively few locations and some locations with many missing measures. We propose a probabilistic predictive PCA (ProPrPCA) that allows for flexible imputation to utilize all available monitoring data. We demonstrate the performance of ProPrPCA with simulations and analysis of multivariate air pollution data.

Authors who are presenting talks have a * after their name.

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