2 papers
stat.ML2025
Fast Riemannian-manifold Hamiltonian Monte Carlo for hierarchical Gaussian-process models
Takashi Hayakawa, Satoshi Asai
Hierarchical Bayesian models based on Gaussian processes are considered useful for describing complex nonlinear statistical dependencies among variables in real-world data. However…
stat.ML2025
Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustment
Takashi Hayakawa, Satoshi Asai
Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the unspecified baseline hazard of the model fails…