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

Key Points

• Estimation of the conditional distribution function was effectively achieved using Wasserstein GAN.
• The approach demonstrates high accuracy in modeling distributions for current status data.
• Wasserstein GAN methodology leverages machine learning principles for improved estimations.
• Results highlight the potential for advanced estimation techniques in statistical modeling.