Statistical Analysis of the 2024 U.S. Presidential Election: Demographics and Swing States
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00646627" target="_blank" >RIV/67985556:_____/25:00646627 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/25:00645857
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Statistical Analysis of the 2024 U.S. Presidential Election: Demographics and Swing States
Original language description
This paper provides an analysis of the 2024 U.S. presidential election using advanced statistical techniques. The study models the popular vote as a response to eight demographic predictors at the state-wide level, incorporating results from the 2020 election to enhance the analysis. A particular focus is given to the application of two recently developed tools inspired by the least weighted squares estimator (LWS): LWS-lasso estimator and LWSquantiles, which are robust methods designed to handle datasets under multicollinearity, heteroscedasticity, and the presence of outliers. The findings emphasize the critical influence of demographic factors in shaping electoral outcomes, illustrating how demographic shifts impact the dynamics of the 2024 election. Special attention is given to the results in seven key swing states, offering precise insights into their pivotal roles in the electoral landscape. Based on the analysis, we propose a novel classification of the swing states into three distinct clusters, taking into account both their demographic outlyingness and their role in the linear model, offering new insights into their strategic importance in the electoral process.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA24-10078S" target="_blank" >GA24-10078S: New nonparametric tools for econometric data analysis</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
RELIK 2025 Conference Proceedings
ISBN
978-80-245-2571-6
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
181-190
Publisher name
University of Economics and Business
Place of publication
Prague
Event location
Prague
Event date
Nov 13, 2025
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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