Gelfand Alan E, Banerjee Sudipto
Department of Statistical Science, Duke University, Durham, North Carolina 27708-0251.
Department of Biostatistics, University of California, Los Angeles, California 90095-1772.
Annu Rev Stat Appl. 2017 Mar;4:245-266. doi: 10.1146/annurev-statistics-060116-054155. Epub 2016 Nov 28.
The most prevalent spatial data setting is, arguably, that of so-called geostatistical data, data that arise as random variables observed at fixed spatial locations. Collection of such data in space and in time has grown enormously in the past two decades. With it has grown a substantial array of methods to analyze such data. Here, we attempt a review of a fully model-based perspective for such data analysis, the approach of hierarchical modeling fitted within a Bayesian framework. The benefit, as with hierarchical Bayesian modeling in general, is full and exact inference, with proper assessment of uncertainty. Geostatistical modeling includes univariate and multivariate data collection at sites, continuous and categorical data at sites, static and dynamic data at sites, and datasets over very large numbers of sites and long periods of time. Within the hierarchical modeling framework, we offer a review of the current state of the art in these settings.
可以说，最普遍的空间数据设置是所谓的地理统计数据，即作为在固定空间位置观测到的随机变量而产生的数据。在过去二十年中，这种数据在空间和时间上的收集量大幅增长。随之而来的是大量用于分析此类数据的方法。在此，我们尝试从完全基于模型的角度对这种数据分析进行综述，即在贝叶斯框架内进行层次建模的方法。与一般的层次贝叶斯建模一样，其好处是能够进行全面且精确的推断，并能对不确定性进行适当评估。地理统计建模包括在站点的单变量和多变量数据收集、站点的连续和分类数据、站点的静态和动态数据，以及大量站点和长时间的数据集。在层次建模框架内，我们对这些设置下的当前技术水平进行综述。
