Summary:
Ozone concentrations are affected by precursor emissions and by meteorological conditions. As part of a broad study to assess the effects of standards imposed by the U.S. Environmental Protection Agency (EPA), it is of interest to analyze trends in ozone after adjusting for meteorological influences. Previous papers have studied this problem for ozone data from Chicago, using a variety of regression techniques. This paper presents a different approach, in which the meteorological influence is treated nonlinearly through a regression tree. A particular advantage of this approach is that it allows us to consider different trends within the clusters produced by the regression tree analysis. The variability of trend estimates between clusters is reduced by applying an empirical Bayes adjustment. The results show that there is downward overall trend throughout the process, but this trend is stronger at higher levels of ozone.
Keywords:
ANOVA; Empirical Bayes; Regression tree.
tr72.pdf