Transfer learning for Box–Cox transformed spatial autoregressive models

Robust transfer learning for spatial expectile regression: linear and semi-parametric additive models

Double/Debiased Machine Learning for Functional-Form-Robust Spatial Autoregression

This paper develops double/debiased machine learning inference for low-dimensional SAR parameters when the spatial interaction operator is learned flexibly from potentially endogenous characteristics.

Unpacking spatial dependence: a new experimental design for spatial autoregressive simulation

Simulating spatial patterns with varying levels of spatial autocorrelation is essential for evaluating models, estimators, and diagnostics. The spatial autoregressive (SAR) process is widely used for this purpose, but its main parameter affects both spatial autocorrelation and spatial heterogeneity. Only the former is...

Restricted multivariate conditional autoregressive models for disease mapping

Bayesian spatial and spatiotemporal models are widely used in disease mapping to stabilize rate estimates in sparse-count settings by pooling information across regions, subgroups, and time. Yet this pooling carries a risk: such models can end up reflecting their own structure more than the data. In other words, they e...

Modeling Spatial Extremes Using Non‐Gaussian Spatial Autoregressive Models via Convolutional Neural Networks

A spatial autoregressive modeling framework, which maps observations at a location and its neighbors to independent random variables, and applies this model to analyze annual maximum precipitation data from ERA‐Interim‐driven Weather Research and Forecasting simulations, allowing it to explore its spatial extreme behav...

Dynamic Spatial Bayesian Machine Learning Model: Applications to Intergenerational Economic Mobility and Geographic Income Inequality in the United States

We develop a Dynamic Spatial Panel Bayesian Additive Regression Trees model with Horseshoe shrinkage (DSP-BART-HS) for high-dimensional spatio-temporal panel data. We jointly evaluate the model against a comprehensive suite of structural spatial econometrics, non-parametric machine learning methods, and small-area esti...