This paper proposes a novel family of geostatistical models to account for features that cannot be properly accommodated by traditional Gaussian processes. The family is specified hierarchically and combines the infinite dimensional dynamics of Gaussian processes to that of any multivariate continuous distribution. This combination is stochastically defined through a latent Poisson process and the new family is called the Poisson-Gaussian Mixture Process - POGAMP. Whilst the attempt of defining a geostatistical process by assigning some arbitrary continuous distributions to be the finite-dimension distributions usually leads to non-valid processes, the finite-dimensional distributions of the POGAMP can be arbitrarily close to any continuous distribution and still define a valid process. Formal results to establish the existence and some important properties of the POGAMP, such as absolute continuity with respect to a Gaussian process measure, are provided. Also, a MCMC algorithm is carefully devised to perform Bayesian inference when the POGAMP is discretely observed in some space domain. Simulations are performed to empirically investigate the modelling properties of the POGAMP and the efficiency of the MCMC algorithm.
翻译:本文建议建立一个新型的地理统计模型体系,以说明传统高斯进程无法适当容纳的特征。 家庭按等级划分,并将高斯进程的无限维度动态与任何多变连续分布过程的无限维度动态结合起来。 这种结合是通过潜伏的 Poisson 进程和新家族(称为PoGAMP) 的绝对连续性等形式界定的。 虽然试图界定一个地理统计过程,将某些任意连续的分布指定为通常导致非有效过程的有限二门分布,但POGAMP的有限维度分布可任意接近任何连续分布,并仍然界定一个有效的过程。 提供了确定POGAMP的存在和一些重要特性的正式结果,例如与高斯进程测量测量测量的绝对连续性。 另外,当POGAMP在某些空间域被独立观察到时, MC的算法经过仔细设计,以进行巴耶斯的推断。 正在对POGAMP的模型进行模拟研究。