New approach to regression in certain time/space series problems. Technical report No. 11. [Dose-response relationships for air pollution]
This paper introduces a new method for estimating a dose-response relationship from spatially averaged time series of air pollution and health data. Because time is perceived as a nuisance parameter to be eliminated, least-squares regression and traditional time series methodology (e.g., spectral analysis, Box-Jenkins methods) are rejected in favor of a nonparametric estimation procedure based on observing health effects in times of nearly equal pollution. The method requires estimating the ratio of two density functions and avoids problems of aggregation, linearity, and normality. The procedure seems most useful, at present, as a data analytic and data display device rather than as an inferential tool.
- Research Organization:
- Stanford Univ., Calif. (USA). Dept. of Statistics
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 5332875
- Report Number(s):
- COO-2874-23
- Country of Publication:
- United States
- Language:
- English
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