🤖 AI Summary
Interval-censored data are prevalent in survival analysis, yet conventional boosting methods struggle to handle them effectively. This work proposes a nonparametric boosting approach tailored for such data, uniquely integrating an unbiased transformation with functional gradient descent. By designing a customized loss function and employing imputation of response variables, the method enables scalable regression and classification modeling. Theoretical analysis elucidates the trade-off between mean squared error and optimality, while empirical experiments demonstrate that the approach is robust under finite-sample settings and substantially improves predictive accuracy. These advantages underscore its practical utility in domains such as medicine and engineering.
📝 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 a robust framework for enhancing predictive accuracy in domains where interval-censored data are common but also complement existing work, expanding the applicability of existing boosting techniques. Empirical studies demonstrate robust performance across various finite-sample scenarios, highlighting the practical utility of our approaches.