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[2601.17973] Boosting methods for interval-censored data with regression and classification
Summary
This article presents novel nonparametric boosting methods tailored for interval-censored data, enhancing regression and classification tasks in various fields such as medical research and social sciences.
Why It Matters
Interval-censored data is prevalent in many real-world applications, particularly in survival analysis. Traditional boosting methods often fail to address this issue effectively. The proposed methods improve predictive accuracy and expand the applicability of boosting techniques, making them valuable for researchers and practitioners dealing with censored data.
Key Takeaways
- Introduces nonparametric boosting methods for interval-censored data.
- Enhances regression and classification tasks, crucial in survival analysis.
- Utilizes censoring unbiased transformations to improve model accuracy.
- Demonstrates scalability and adaptability through functional gradient descent.
- Empirical studies confirm robust performance across various scenarios.
Statistics > Machine Learning arXiv:2601.17973 (stat) [Submitted on 25 Jan 2026 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Boosting methods for interval-censored data with regression and classification Authors:Yuan Bian, Grace Y. Yi, Wenqing He View a PDF of the paper titled Boosting methods for interval-censored data with regression and classification, by Yuan Bian and 2 other authors View PDF HTML (experimental) Abstract:Boosting has garnered significant interest across both machine learning and statistical communities. Traditional boosting algorithms, designed for fully observed random samples, often struggle with real-world problems, particularly with interval-censored data. This type of data is common in survival analysis and time-to-event studies where exact event times are unobserved but fall within known intervals. Effective handling of such data is crucial in fields like medical research, reliability engineering, and social sciences. In this work, we introduce novel nonparametric boosting methods for regression and classification tasks with interval-censored data. Our approaches leverage censoring unbiased transformations to adjust loss functions and impute transformed responses while maintaining model accuracy. Implemented via functional gradient descent, these methods ensure scalability and adaptability. We rigorously establish their theoretical properties, including optimality and mean squared error trade-offs. Our proposed methods not only offer ...