Summary of Deep Non-parametric Logistic Model with Case-control Data and External Summary Information, by Hengchao Shi et al.
Deep non-parametric logistic model with case-control data and external summary information
by Hengchao Shi, Ming Zheng, Wen Yu
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach for estimating a non-parametric logistic model in case-control sampling designs with external summary information is proposed. This method uses two steps: first, estimating the marginal case proportion from the external information, and then using this estimate to construct a weighted objective function for parameter training. A deep neural network architecture is employed for functional approximation. Theoretical analysis provides a non-asymptotic error bound and convergence rate of the proposed estimator. Simulation studies validate these findings, and a real data example illustrates the method’s effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with some types of data by using extra information to help estimate a model. They take two steps: first, they figure out how often certain things happen in general, and then they use that information to make predictions about specific cases. They also show that their approach is reliable and efficient. This has applications in many fields where data is imbalanced or hard to understand. |
Keywords
» Artificial intelligence » Neural network » Objective function
