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

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...

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Veröffentlicht in:	arXiv.org 2024-09
Hauptverfasser:	Zeng, Hao, Zhong, Wei, Xu, Xingbai
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Autoregressive models Data analysis Election results Elections Learning Parameter estimation Predictions Presidential elections Spatial data
Online-Zugang:	Volltext
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