Computationally Simplified Estimators for Spatial Autoregressive Models

回归是社会科学中广泛使用的一种统计方法，但大多数应用于空间参考数据的回归分析考虑了潜在的空间自相关性，假设误差项为正态分布，并涉及最大似然估计。在传统的统计分析中，这种情况会导致非线性结构，导致估计过程需要缓慢的迭代解。这使得空间回归计算密集，从而减缓了空间统计和空间计量经济学技术的传播。本项目将探索空间回归中对这一归一化因素的有用近似，以简化对中型和大型数据集的分析，并展示这些简化的效用。该方法将包括使用定时函数的模拟实验，以评估减少数值强度的成本和收益。实验将在超级计算机环境中进行。该项目将有助于消除传播空间统计程序的障碍，并应减少在社会科学研究中使用空间统计通常所涉及的数字密集程度。

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.