Understanding extreme behavior by optimizing tail dependence with application to ground level ozone via data mining and spatial modeling
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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 ...