Convergence Rates for Neural-Network Estimation with Current-Status Data

Yuan Wu; Tianhui Zhou

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arXiv · 2606.10119

Convergence Rates for Neural-Network Estimation with Current-Status Data

Yuan Wu·Tianhui Zhou

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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 estimation and subsequent inference under current-status observation.

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BibTeX RIS

Yuan Wu, Tianhui Zhou. 2026-06-08. Convergence Rates for Neural-Network Estimation with Current-Status Data. https://arxiv.org/abs/2606.10119

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