Abstract

This paper develops a Bayesian nonparametric model for skewed spatial data with nonstationary dependence structure. A transformed Gaussian model is proposed for the atoms of the kernel stick-breaking process by transforming the margins of a Gaussian process to flexible marginal distributions. This study proves that the correlation structure of the underlying spatial process is nonstationary. Results from both simulated and real datasets demonstrate that the proposed model possesses better spatial prediction performance and offers computational advantages compared to the Bayesian nonparametric model with the Gaussian base measure.

中文翻译：

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广义空间断棍过程

摘要

本文针对具有非平稳依赖结构的倾斜空间数据开发了贝叶斯非参数模型。通过将高斯过程的边缘转换为灵活的边缘分布，提出了一种用于核断棒过程的原子的转换高斯模型。这项研究证明了底层空间过程的相关结构是非平稳的。模拟数据集和真实数据集的结果表明，与具有高斯基测度的贝叶斯非参数模型相比，所提出的模型具有更好的空间预测性能并具有计算优势。