arXiv:2606.10119v1 Announce Type: cross Abstract: Current-status data arise when an event time is observed only through an indicator of whether it occurred before an examination time. This paper studies a nonparametric neural-network sieve maximum likelihood estimator of the conditional cumulative distribution function of the event time. Under H\"older smoothness assumptions, we establish an explicit convergence rate by combining approximation theory for rectified linear unit neural networks with empirical-process arguments. This result provides theoretical support for neural-network estimatio
This paper represents a continuing trend of increased theoretical rigor and mathematical understanding applied to neural network performance, particularly in statistical estimation contexts.
Advanced theoretical support for neural network estimation improves their reliability and expands their application to complex, real-world data scenarios, including those with incomplete information.
The explicit convergence rates for neural networks in current-status data settings enhance the scientific foundation for using AI in areas like survival analysis and actuarial science.
- · AI researchers
- · Statisticians
- · Healthcare analytics
- · Financial modeling
- · Traditional statistical methods
- · Researchers relying solely on empirical performance
Improved statistical accuracy and trustworthiness of neural network models in scenarios with incomplete data.
Broader adoption of deep learning in fields previously reliant on less robust statistical methods due to enhanced theoretical guarantees.
Accelerated development of AI systems capable of handling more nuanced and complex data challenges with greater confidence in their outcomes.
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