Monte Carlo Methods for Analysis of Large Spatial Data

用于分析大空间数据的蒙特卡罗方法

• 批准号: 1545738
• 负责人: Faming Liang
• 金额: $ 3.88万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-02 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545738&HistoricalAwards=false
• 关键词: Monte; Carlo; Methods; Analysis; Large

Spatial data sets are analyzed in many scientific disciplines, such as ecology, geology, and environmental sciences. However, the classical approaches, such as Kriging and Bayesian hierarchical Gaussian modeling, often break down for large data sets due to expensive matrix inverse operations, whose computational complexity increases in cubic order with the number of spatial locations. To alleviate this difficulty, various approximation approaches, such as covariance tapering, lower-dimensional space spatial process approximation, likelihood approximation and Markov random field approximations, have been proposed under the general idea of approximating the original spatial model with a computationally convenient model. A general concern on these approaches is the adequacy of approximation. In this proposal, the investigators propose three new approaches, Bayesian auxiliary lattice approach, Bayesian site selection approach and marginal inference approach. The Bayesian auxiliary lattice approach introduces an auxiliary lattice to the space of observations and defines a hidden Gaussian Markov random field on the auxiliary lattice. By using some analytical results of Gaussian Markov random fields, the Bayesian auxiliary lattice approach completely avoids the problem of matrix inversion in likelihood evaluation. The Bayesian site selection approach reformulates the problem of spatial model estimation as a problem of Bayesian variable selection. It works with only a small proportion of the data at each iteration and thus significantly reduces the dimension of the data. The marginal inference approach is proposed based on the idea of bootstrap resampling. Like the Bayesian site selection approach, it works with only a small proportion of the data at each iteration and thus significantly reduces the dimension of the data. It is worth noting that the Bayesian site selection and marginal inference approaches are conceptually very different from the approximation approaches existing in the literature. The existing approximation approaches are to approximate the original model using a computationally convenient model. Instead, the Bayesian site selection and marginal inference approaches seek to reduce the dimension of the data, while not sacrificing the complexity of the original model. In this proposal, the investigators also extend the proposed approaches to spatio-temporal models with applications to satellite climate data. How to deal with missing data for spatio-temporal models are addressed.The intellectual merit of this project is to provide some computationally efficient or data dimension reduction approaches for statistical analysis of large spatial data. The new approaches address some core problems in spatial data analysis, such as large matrix inversion and missing data imputation. The new approaches are expected to play a major role in statistical analysis of geostatistical data, satellite climate data and other large spatial data. This project will have broader impacts in both communities of spatial statistics and computational atmospheric sciences. The research results will be disseminated to the communities via direct collaboration with researchers in other disciplines, conference presentations, books, and papers to be published in academic journals. The project will have also significant impacts on education through direct involvement of graduate students in the project and incorporation of results into undergraduate and graduate courses.

空间数据集在许多科学学科中进行分析，例如生态学、地质学和环境科学。 然而，克里金法和贝叶斯分层高斯建模等经典方法经常因昂贵的矩阵逆运算而无法处理大型数据集，其计算复杂度随着空间位置数量的增加而按三次方增加。为了缓解这一困难，在用计算方便的模型来逼近原始空间模型的总体思想下，提出了各种逼近方法，例如协方差锥化、低维空间空间过程逼近、似然逼近和马尔可夫随机场逼近。对这些方法的普遍关注是近似的充分性。在该提案中，研究人员提出了三种新方法：贝叶斯辅助格方法、贝叶斯选址方法和边际推理方法。贝叶斯辅助格方法将辅助格引入观测空间，并在辅助格上定义隐藏的高斯马尔可夫随机场。 贝叶斯辅助格方法利用高斯马尔可夫随机场的一些分析结果，完全避免了似然评估中的矩阵求逆问题。贝叶斯选址方法将空间模型估计问题重新表述为贝叶斯变量选择问题。它在每次迭代时仅处理一小部分数据，从而显着降低了数据的维度。边际推理方法是基于自举重采样的思想提出的。与贝叶斯选址方法一样，它在每次迭代时仅处理一小部分数据，从而显着降低了数据的维度。值得注意的是，贝叶斯选址和边际推理方法在概念上与文献中存在的近似方法有很大不同。现有的近似方法是使用计算方便的模型来近似原始模型。相反，贝叶斯选址和边际推理方法寻求减少数据的维度，同时不牺牲原始模型的复杂性。在这项提案中，研究人员还将所提出的方法扩展到时空模型，并将其应用于卫星气候数据。讨论了如何处理时空模型的缺失数据。该项目的智力价值是为大型空间数据的统计分析提供一些计算效率或数据降维的方法。新方法解决了空间数据分析中的一些核心问题，例如大矩阵求逆和缺失数据插补。新方法预计将在地统计数据、卫星气候数据和其他大空间数据的统计分析中发挥重要作用。该项目将对空间统计和计算大气科学界产生更广泛的影响。研究成果将通过与其他学科研究人员的直接合作、会议演讲、书籍和在学术期刊上发表的论文来传播给社区。 该项目还将通过研究生直接参与该项目并将成果纳入本科生和研究生课程，对教育产生重大影响。