# Transfer Learning for Spatial Autoregressive Models with Application to U.S. Presidential Election Prediction

arXiv: https://arxiv.org/abs/2405.15600 · HTML: https://mlsanity.com/paper/2405.15600/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 11 of 11)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Transfer Learning for High-dimensional Linear Regression: Prediction, Estimation, and Minimax Optimality](https://mlsanity.com/paper/2006.10593/) (2020)
- [Transfer Learning under High-dimensional Generalized Linear Models](https://mlsanity.com/paper/2105.14328/) (2021)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)
- [False Discoveries Occur Early on the Lasso Path](https://mlsanity.com/paper/1511.01957/) (2015)
- [Predicting with Proxies: Transfer Learning in High Dimension](https://mlsanity.com/paper/1812.11097/) (2018)
- [Transfer Learning for Contextual Multi-armed Bandits](https://mlsanity.com/paper/2211.12612/) (2022)
- [Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure](https://mlsanity.com/paper/2401.12272/) (2024)
- [Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach](https://mlsanity.com/paper/2108.12112/) (2021)
- [Transfer Learning for High-dimensional Quantile Regression via Convolution Smoothing](https://mlsanity.com/paper/2212.00428/) (2022)
- [Estimation and Inference with Proxy Data and its Genetic Applications](https://mlsanity.com/paper/2201.03727/) (2022)

## Built on by (most cited first, 1 of 1)

- [Multi-Source Transfer Learning of Sparse Single-Index Models](https://mlsanity.com/paper/2606.29658/) (2026)

## Abstract (arXiv)

It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant challenges of limited spatial data availability. To address the challenges of spatial dependence and small sample sizes in predicting U.S. presidential election results using spatially dependent data, we propose a novel transfer learning framework within the SAR model, called as tranSAR. Classical SAR model estimation often loses accuracy with small target data samples. Our framework enhances estimation and prediction by leveraging information from similar source data. We introduce a two-stage algorithm, consisting of a transferring stage and a debiasing stage, to estimate parameters and establish theoretical convergence rates for the estimators. Additionally, if the informative source data are unknown, we propose a transferable source detection algorithm using spatial residual bootstrap to maintain spatial dependence and derive its detection consistency. Simulation studies show our algorithm substantially improves the classical two-stage least squares estimator. We demonstrate our method's effectiveness in predicting outcomes in U.S. presidential swing states, where it outperforms traditional methods. In addition, our tranSAR model predicts that the Democratic party will win the 2024 U.S. presidential election.