Spatiotemporal modelling using integro-difference equations with bivariate stable kernels

• 成果类型: Article
• 署名作者: Richardson, Robert; Kottas, Athanasios; Sanso, Bruno
• 署名单位: Brigham Young University; University of California System; University of California Santa Cruz
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12393
• 发表日期: 2020
• 页码: 1371-1392
• 关键词: EL-NINO; prediction; forecasts; FIELDS; temperatures

摘要

An integro-difference equation can be represented as a hierarchical spatiotemporal dynamic model using appropriate parameterizations. The dynamics of the process defined by an integro-difference equation depends on the choice of a bivariate kernel distribution, where more flexible shapes generally result in more flexible models. Under a Bayesian modelling framework, we consider the use of the stable family of distributions for the kernel, as they are infinitely divisible and offer a variety of tail behaviours, orientations and skewness. Many of the attributes of the bivariate stable distribution are controlled by a measure, which we model using a flexible Bernstein polynomial basis prior. The method is the first attempt to incorporate non-Gaussian kernels in a two-dimensional integro-difference equation model and will be shown to improve prediction over the Gaussian kernel model for a data set of Pacific sea surface temperatures.