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作者：Zheng, Yanbing; Zhu, Jun; Roy, Anindya
作者单位：University of Kentucky; University of Wisconsin System; University of Wisconsin Madison; University System of Maryland; University of Maryland Baltimore County
摘要：A powerful technique for inference concerning spatial dependence in a random field is to use spectral methods based on frequency domain analysis. Here we develop a nonparametric Bayesian approach to statistical inference for the spectral density of a random field. We construct a multi-dimensional Bernstein polynomial prior for the spectral density and devise a Markov chain Monte Carlo algorithm to simulate from the posterior of the spectral density. The posterior sampling enables us to obtain ...
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作者：Apanasovich, Tatiyana V.; Genton, Marc G.
作者单位：Thomas Jefferson University; Texas A&M University System; Texas A&M University College Station
摘要：The problem of constructing valid parametric cross-covariance functions is challenging. We propose a simple methodology, based on latent dimensions and existing covariance models for univariate random fields, to develop flexible, interpretable and computationally feasible classes of cross-covariance functions in closed form. We focus on spatio-temporal cross-covariance functions that can be nonseparable, asymmetric and can have different covariance structures, for instance different smoothness...
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作者：Szabo, Aniko; George, E. Olusegun
作者单位：Medical College of Wisconsin; University of Memphis
摘要：We introduce the use of stochastic ordering for defining treatment-related trend in clustered exchangeable binary data for both when cluster sizes are fixed and when they vary randomly. In the latter case, there is a well-documented tendency for such data to be sparse, a problem we address by making an assumption of interpretability or, equivalently, marginal compatibility. Our procedures are based on a representation of the joint distribution of binary exchangeable random variables by a satur...
