Influencing elections with statistics: targeting voters with logistic regression trees

DOI10.1214/13-AOAS648zbMath1454.62505arXiv1311.7326WikidataQ57066591 ScholiaQ57066591MaRDI QIDQ386749

Kurt Hornik, Wolfgang Jank, Ilro Lee, Achim Zeileis, Thomas Rusch

Publication date: 10 December 2013

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1311.7326

zbMATH Keywords

classification treecampaigningget-out-the-votemicrotargetingmodel treepolitical marketingvoter identificationvoter profilevoter segmentation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to social sciences (62P25) Generalized linear models (logistic models) (62J12)

Related Items (2)

Influencing elections with statistics: targeting voters with logistic regression trees ⋮ Modeling threshold interaction effects through the logistic classification trunk

Uses Software

• R
• ROCR
• rpart
• BayesTree
• mobtools
• ElemStatLearn
• BartPy

Cites Work

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• BART: Bayesian additive regression trees
• Influencing elections with statistics: targeting voters with logistic regression trees
• Recursive partitioning and applications
• On the existence of maximum likelihood estimates in logistic regression models
• Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign
• Logistic model trees

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