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Neural network convergence rates analyzed for current-status data

By PulseAugur Editorial · [2 sources] ·

Researchers have published a paper detailing convergence rates for neural network estimators when dealing with current-status data. This type of data is collected when an event's occurrence is only known relative to an observation time, not its precise timing. The study introduces a nonparametric sieve maximum likelihood estimator and provides theoretical backing for its use in estimating event time distributions. AI

IMPACT Provides theoretical support for neural network estimation techniques in specific data scenarios.

RANK_REASON The cluster contains an academic paper published on arXiv.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Yuan Wu, Tianhui Zhou ·

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

    arXiv:2606.10119v1 Announce Type: new 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 condit…

  2. arXiv stat.ML TIER_1 English(EN) · Tianhui Zhou ·

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

    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 ev…

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