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