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Physical processes that manifest as tangential vector fields on a sphere are common in geophysical and environmental sciences. These naturally occurring vector fields are often subject to physical constraints, such as being curl-free or divergence-free. We construct a new class of parametric models for cross-covariance functions of curl-free and divergence-free vector fields that are tangential to the unit sphere. These models are constructed by applying the surface gradient or the surface curl operator to scalar random potential fields defined on the unit sphere. We propose a likelihood-based estimation procedure for the model parameters and show that fast computation is possible even for large data sets when the observations are on a regular latitude-longitude grid. Characteristics and utility of the proposed methodology are illustrated through simulation studies and by applying it to an ocean surface wind velocity data set collected through satellite-based scatterometry remote sensing. We also compare the performance of the proposed model with a class of bivariate Matern models in terms of estimation and prediction, and demonstrate that the proposed model is superior in capturing certain physical characteristics of the wind fields.
The joint modeling of mean and dispersion (JMMD) provides an efficient method to obtain useful models for the mean and dispersion, especially in problems of robust design experiments. However, in the literature on JMMD there are few works dedicated t
Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We demonstrate that SVM can be used to balance covariates and estimate average causal effects under the unconfoundedness assumption.
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Dialect variation is of considerable interest in linguistics and other social sciences. However, traditionally it has been studied using proxies (transcriptions) rather than acoustic recordings directly. We introduce novel statistical techniques to a
We propose a distributed quadratic inference function framework to jointly estimate regression parameters from multiple potentially heterogeneous data sources with correlated vector outcomes. The primary goal of this joint integrative analysis is to