Bayesian Modeling and Analysis of Geostatistical Data.
Bayesian Modeling and Analysis of Geostatistical Data.
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DOI：
10.1146/annurev-statistics-060116-054155
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发表时间：
2017-03
影响因子：
7.9
通讯作者：
Banerjee S
中科院分区：
文献类型：
--
作者：
Gelfand AE;Banerjee S
可以说，最常见的空间数据情形是所谓的地统计数据，即作为在固定空间位置观测到的随机变量而产生的数据。在过去的二十年中，此类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，我们尝试综述一种基于完全模型的此类数据分析视角，即贝叶斯框架内的分层建模方法。与一般的分层贝叶斯建模一样，其优点是能够进行全面而精确的推断，并对不确定性进行恰当评估。地统计建模包括在地点处的单变量和多变量数据收集、地点处的连续和分类数据、地点处的静态和动态数据，以及涉及大量地点和长时间的数据集。在分层建模框架内，我们综述了这些情形下的当前技术水平。
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.