Spatial Matérn Fields Driven by Non‐Gaussian Noise
研究了非高斯噪声驱动的Matérn协方差随机场，利用随机偏微分方程构建拉普拉斯移动平均模型，并给出高效模拟和参数估计方法，对空间统计和随机场研究者有用。
Abstract The article studies non‐Gaussian extensions of a recently discovered link between certain Gaussian random fields, expressed as solutions to stochastic partial differential equations (SPDEs), and Gaussian Markov random fields. The focus is on non‐Gaussian random fields with Matérn covariance functions, and in particular, we show how the SPDE formulation of a Laplace moving‐average model can be used to obtain an efficient simulation method as well as an accurate parameter estimation technique for the model. This should be seen as a demonstration of how these techniques can be used, and generalizations to more general SPDEs are readily available.