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ISSUES IN SEMI-PARAMETRIC REGRESSION WITH APPLICATIONS IN TIME SERIES MODELS FOR AIR POLLUTION AND MORTALITY

In 2002, methodological issues around time series analyses of air pollution and health attracted the attention of statisticians, epidemiologists, the press, industry, and policy makers. As the Environmental Protection Agency (EPA) was finalizing its review of the evidence on particulate air pollution, statisticians and epidemiologists found that the S-Plus implementation of Generalized Additive Models (GAM), the most common statistical approach for time series analysis of air pollution and health, can overestimate eects of air pollution and understate statistical uncertainty. This discovery delayed the process for the Particulate Matter (PM) Criteria Document prepared as part of the review of the National Ambient Air Quality Standard (NAAQS), as the time-series findings were a critical component of the evidence. In addition, it raised concerns about the adequacy of current model formulations and their software implementations.