Abstract #300325

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JSM 2003 Abstract #300325
Activity Number: 429
Type: Invited
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Risk Analysis
Abstract - #300325
Title: Issues in Semiparametric Regression: A Case Study of Time Series Models of Air Pollution and Mortality
Author(s): Francesca Dominici*+ and Aidan McDermott and Trevor J. Hastie
Companies: Johns Hopkins University and Johns Hopkins University and Stanford University
Address: 615 N. Wolfe St., Baltimore, MD, 21205-2103,
Keywords: semiparametric regression ; Generalized Additive Models ; air pollution ; time series ; mean squared error ; regression splines
Abstract:

We provide two methodological results in semiparametric regression directly relevant to risk estimation of air pollution. First, we introduce a closed form estimate of the asymptotically exact covariance matrix of the linear part of a Generalized Additive Model (GAM) and we develop the S-plus package GAM.exact(), an extended version of GAM(). Second, we introduce a semiautomated procedure for selecting the degrees of freedom in the smooth part of the model that minimizes the mean squared error of the regression coefficients corresponding to the linear part. In time series analyses of air pollution and health, our semiautomated procedure provides a diagnostic tool for choosing the degree of adjustment for time-varying factors that might confound the pollution-mortality relationship. We apply our methods to the National Mortality Morbidity Air Pollution Study (NMMAPS), which includes time series data from the 90 largest US locations for the period 1987-1994.

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