Heaton Matthew J, Datta Abhirup, Finley Andrew O, Furrer Reinhard, Guinness Joseph, Guhaniyogi Rajarshi, Gerber Florian, Gramacy Robert B, Hammerling Dorit, Katzfuss Matthias, Lindgren Finn, Nychka Douglas W, Sun Furong, Zammit-Mangion Andrew
Brigham Young University, Provo, UT USA.
J Agric Biol Environ Stat. 2019;24(3):398-425. doi: 10.1007/s13253-018-00348-w. Epub 2018 Dec 14.
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit low-rank structures and/or multi-core and multi-threaded computing environments to facilitate computation. This study provides, first, an introductory overview of several methods for analyzing large spatial data. Second, this study describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology. Specifically, each research group was provided with two training datasets (one simulated and one observed) along with a set of prediction locations. Each group then wrote their own implementation of their method to produce predictions at the given location and each was subsequently run on a common computing environment. The methods were then compared in terms of various predictive diagnostics. Supplementary materials regarding implementation details of the methods and code are available for this article online.
Supplementary materials for this article are available at 10.1007/s13253-018-00348-w.
高斯过程是空间数据分析中不可或缺的工具。然而，“大数据”时代的到来使得传统高斯过程在处理现代空间数据时计算上变得不可行。因此，人们提出了各种更适合处理大规模空间数据的全高斯过程替代方法。这些现代方法通常利用低秩结构和/或多核多线程计算环境来促进计算。本研究首先对几种分析大型空间数据的方法进行了介绍性概述。其次，本研究描述了由不同方法学专业团队实现的所述方法之间预测竞赛的结果。具体而言，为每个研究团队提供了两个训练数据集（一个模拟数据集和一个观测数据集）以及一组预测位置。然后，每个团队编写自己的方法实现代码，以便在给定位置进行预测，随后每个实现代码在一个通用计算环境上运行。然后根据各种预测诊断对这些方法进行比较。本文在线提供了有关方法实现细节和代码的补充材料。
本文的补充材料可在10.1007/s13253-018-00348-w获取。
