arbiter
JournalsPricingAccount

Wasserstein GAN-based estimation for conditional distribution function with current status data

Wen Su et al.

Lifetime Data Analysis: an international journal devoted to the methods and applications of reliability and survival analysis2026https://doi.org/10.1007/s10985-026-09691-4article
ABDC B
Weight
0.37

What the paper says

Current status data are commonly encountered in modern medicine, econometrics and social science. Its unique characteristics pose significant challenges to the analysis of such data and the existing methods often suffer grave consequences when the underlying model is misspecified. To address these difficulties, we propose a model-free two-stage generative approach for estimating the conditional cumulative distribution function given predictors. We first learn a conditional generator nonparametrically for the joint conditional distribution of observation times and event status, and then construct the nonparametric maximum likelihood estimators of conditional distribution functions based on samples from the conditional generator. Subsequently, we study the convergence properties of the proposed estimator and establish its consistency. Simulation studies under various settings show the superior performance of the deep conditional generative approach over the classical modeling approaches and an application to Parvovirus B19 seroprevalence data yields reasonable predictions.

1 citation

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1007/s10985-026-09691-4

Or copy a formatted citation

@article{wen2026,
  title        = {{Wasserstein GAN-based estimation for conditional distribution function with current status data}},
  author       = {Wen Su et al.},
  journal      = {Lifetime Data Analysis: an international journal devoted to the methods and applications of reliability and survival analysis},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10985-026-09691-4},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Wasserstein GAN-based estimation for conditional distribution function with current status data

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.