Computationally Simplified Estimators for Spatial Autoregressive Models

空间自回归模型的计算简化估计器

• 批准号: 9122232
• 负责人: Daniel Griffith
• 金额: $ 7.2万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-07-01 至 1994-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9122232&HistoricalAwards=false
• 关键词: Computationally; Simplified; Estimators; Spatial; Autoregressive

Regression is a widely used statistical technique in the social sciences, but most regression analyses applied to spatially referenced data that take account of latent spatial autocorrelation assume normally distributed error terms and involve maximum likelihood estimation. In traditional statistical analysis this situation results in a nonlinear structure, leading to estimation procedures that require slow iterative solutions. This makes spatial regression computationally intensive, thus slowing the dissemination of spatial statistics and spatial econometrics techniques. This project will explore useful approximations to this normalizing factor in spatial regression to simplify the analysis of intermediate and large size data sets and to demonstrate the utility of these simplifications. The approach will involve simulation experiments with timing functions to evaluate the costs and benefits of reductions in numerical intensity. Experiments will be conducted in a supercomputer environment. This project will assist in the removal of barriers to the dissemination of spatial statistical procedures, and it should reduce the numerical intensity normally associated with the use of spatial statistics in social science research.

回归是一种广泛使用的统计技术， 社会科学，但大多数回归分析适用于 空间参考数据，考虑到潜在的空间 自相关假设正态分布的误差项， 涉及最大似然估计。 传统 统计分析这种情况导致非线性 结构，导致估计程序，需要缓慢 迭代解 这使得空间回归 计算密集型，从而减缓传播 空间统计学和空间计量经济学技术。 这 项目将探讨有用的近似，这种规范化 空间回归因子，以简化 中型和大型数据集，并证明 利用这些简化。 该方法将涉及 模拟实验与定时功能，以评估 降低数值强度的成本和收益。 实验将在超级计算机环境中进行。 这一项目将有助于消除阻碍 传播空间统计程序，并应 降低通常与使用相关的数值强度 社会科学研究中的空间统计学。