2022/12/12 by Zehao Yu, Yu, Zehao, Xianzheng Huang +1
Computer Science · Environmental Science · #62E15 (Primary) 62F10 (Secondary) #FOS: Computer and information sciences #Geochemistry and Geologic Mapping #Hydrology and Drought Analysis #Methodology (stat.ME) #Soil Geostatistics and Mapping
paper · pdf · doi:10.48550/arxiv.2212.05634
openalex publication_date 2022/12/12 · openalex created_date 2022/12/26 · openalex updated_date 2026/07/28
We formulate a class of angular Gaussian distributions 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 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 goodness of fit of this distribution in a data application. Simulation study and application to data from a hydrogeology study are used to demonstrate implementation and performance of the inference procedures and diagnostics methods.