Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling
This dissertation presents novel work in statistical methods for extremes. Our underlying modeling procedure identifies the linear combination of covariates that is associated with extreme values of a response variable, and is based on the framework of bivariate regular variation. We propose a data mining strategy that is suitable for an analysis of ground level ozone, and spatially model the primary drivers of extreme ozone over a large study region. In this dissertation, we first review statistical methods for univariate and multivariate extremes. We then discuss tail dependence parameters ...
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