🤖 AI Summary
This study addresses the limitations of existing spatial dependence diagnostics—such as Moran’s I and APLE—in effectively assessing residual spatial autocorrelation after adjusting for large-scale trends and covariates. The authors propose RESAPLE, a novel first-order approximation estimator based on restricted maximum likelihood (REML) residuals, to accurately and efficiently estimate the spatial autocorrelation parameter ρ in spatial error models. By innovatively integrating the Rayleigh quotient formulation with REML residuals, RESAPLE combines the interpretability of exploratory spatial indicators with the rigor of statistical estimation, while also offering a diagnostic tool for selecting spatial weight matrices. Under correctly specified trend models and with moderate to small sample sizes, RESAPLE demonstrates superior estimation accuracy and testing power compared to both Moran’s I and APLE, and is applicable to both regular and irregular lattice data.
📝 Abstract
Diagnostics such as Moran's index and approximate profile likelihood-based estimators (APLE) for Gaussian spatial autoregressive models are widely used in exploratory data analysis to assess the strength of spatial dependence. Yet, although Moran's index is often applied to regression residuals, and APLE is typically formulated for raw outcomes, neither is explicitly constructed as an estimator of residual spatial dependence after adjustment for large-scale trends and covariates. We propose RESAPLE, a one-step approximate restricted maximum likelihood (REML) estimator of the spatial error model's spatial dependence parameter $\rho$, constructed from REML residuals. Because RESAPLE is a Rayleigh coefficient, it retains the interpretability and diagnostic convenience of exploratory indices, while also providing a computationally inexpensive and accurate estimator of $\rho$ for moderate dependence. We show that for small to medium sample sizes and adequately specified trend models, RESAPLE is a better estimator of, and test statistic for, residual spatial dependence relative to existing alternatives including Moran's index and the APLE across a wide range of practical settings. The theory we develop also yields a diagnostic for spatial weight selection, providing guidance towards resolving a common point of ambiguity in spatial data analysis. We illustrate the method using simulations on both regular and highly irregular lattices with a case study using American Community Survey tract-level data.