Deep tobit model: an integrated framework for high-dimensional censored regression with variable selection
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces the Deep Tobit model for analyzing high-dimensional, left-censored data. The novel framework enhances variable selection and prediction accuracy, outperforming existing methods.
Area Of Science
- Statistics
- Machine Learning
- Data Science
Background
- High-dimensional data with left-censored responses present analytical challenges.
- Existing methods like classical Tobit models and deep learning have limitations in handling nonlinearity, variable selection, and interpretability.
Purpose Of The Study
- To propose an integrated deep learning framework, the Deep Tobit model, for analyzing high-dimensional left-censored data.
- To develop a robust two-stage feature selection algorithm with theoretical guarantees.
- To improve both variable selection and prediction accuracy in censored data analysis.
Main Methods
- Developed the Deep Tobit model using the negative Tobit log-likelihood as its loss function to address data censoring.
- Implemented a two-stage feature selection algorithm with proven convergence rate and selection consistency.
- Validated the model through extensive simulation studies and real-world applications.
Main Results
- The Deep Tobit model demonstrated superior performance compared to state-of-the-art baselines.
- The framework achieved high accuracy in both variable selection and prediction.
- Successful application to aero-engine vibration and HIV viral load datasets.
Conclusions
- The Deep Tobit model offers a powerful and effective solution for analyzing complex censored data.
- The integrated approach balances prediction performance with essential variable selection and interpretability.
- This framework advances the analysis of high-dimensional left-censored data in various scientific fields.
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