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Title: Elliptically symmetric distributions for directional data of arbitrary dimension Authors:  Zehao Yu - University of South Carolina (United States) [presenting]
Abstract: A class of angular Gaussian distributions is formulated that allows different degrees of isotropy for directional random variables of arbitrary dimension. Through a series of novel reparameterization, this distribution family is indexed by parameters with meaningful statistical interpretations that can range over the entire real space of an adequate dimension. The new parameterization greatly simplifies the maximum likelihood estimation of all model parameters, which in turn leads to theoretically sound and numerically stable inference procedures to infer key features of the distribution. Byproducts from the likelihood-based inference are used to develop graphical and numerical diagnostic tools for assessing the goodness of fit of this distribution in a data application. Simulation study and application to data from a hydrogeology study are used to demonstrate the implementation and performance of the inference procedures and diagnostics methods.