• Corpus ID: 237266725

Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition

@article{Zhan2021ConvexLE,
  title={Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition},
  author={Hongyuan Zhan and Kamesh Madduri and Venkataraman N. Shankar},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.09859}
}
In this paper, we propose a convex formulation for learning logistic regression model (logit) with latent heterogeneous effect on sub-population. In transportation, logistic regression and its variants are often interpreted as discrete choice models under utility theory (McFadden, 2001). Two prominent applications of logit models in the transportation domain are traffic accident analysis and choice modeling. In these applications, researchers often want to understand and capture the individual… 

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