Hao Zeng, Wei Zhong, Xingbai Xu
arXiv 20 May 2024 · Statistics — Machine Learning · publishedJournal of Business and Economic Statistics (2026) · 3 citations (OpenAlex)
arXiv:2405.15600 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Tian, Y. and Feng, Y (2023) Transfer learning under high-dimensional generalized linear models | 1.000 | 11 | 5 | 100% |
| 2 | Bastani, H (2021) Predicting with proxies: Transfer learning in high dimension | 0.644 | 2 | 2 | 100% |
| 3 | Kelejian, H. H. and Prucha, I. R (1999) A generalized moments estimator for the autoregressive parameter in a spatial model | 0.644 | 2 | 2 | 100% |
| 4 | Lee, L.-f (2004) Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models | 0.644 | 2 | 2 | 100% |
| 5 | Lee, L.-f (2007) GMM and 2SLS estimation of mixed regressive, spatial autoregressive models | 0.644 | 2 | 2 | 100% |
| 6 | Li, S., Cai, T. T., and Li, H (2022) Transfer learning for high-dimensional linear regression: Prediction, estimation and minimax optimality | 0.644 | 2 | 2 | 100% |
| 7 | Torrey, L. and Shavlik, J (2010) Transfer learning | 0.644 | 2 | 2 | 100% |
| 8 | Anselin, L (1988) Spatial Econometrics: Methods and Models, volume 4 of Studies in Operational Regional Science | 0.585 | 3 | 1 | 100% |
| 9 | Grennan, J (2019) Dividend payments as a response to peer influence | 0.511 | 2 | 1 | 100% |
| 10 | Kelejian, H. H. and Prucha, I. R (1998) A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive distur… | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 52 scored citations.