5 papers
Self-Normalizing Denominators in Rational Covariance Estimators
Shu Tamano
Many estimators are ratios of coprime polynomials in a sample covariance matrix, and their accuracy depends on the relative fluctuation of the sample denominator. Under Gaussian sa…
Extended Wasserstein-GAN Approach to Causal Distribution Learning: Density-Free Estimation and Minimax Optimality
Shu Tamano, Masaaki Imaizumi
Distributional causal inference requires estimating not only average treatment effects but also interventional outcome distributions, including quantiles, tail risks, and policy-de…
Estimating Consensus Epidemic Trajectories via a Constrained Power Fréchet Mean with Functional Registration
Yui Tomo, Shu Tamano, Daisuke Yoneoka
In infectious disease modeling during the early phase of a pandemic, SEIR-type compartmental models are standard tools, and they require epidemiological parameters as inputs. Becau…
Location--Scale Calibration for Generalized Posterior
Shu Tamano, Yui Tomo
General Bayesian updating replaces the likelihood with a loss scaled by a learning rate, but posterior uncertainty can depend sharply on that scale. We propose a simple post-proces…
Efficient Gibbs Sampling in Cox Regression Models Using Composite Partial Likelihood and Pólya-Gamma Augmentation
Shu Tamano, Yui Tomo
The Cox regression models and their Bayesian extensions are widely used for time-to-event analysis. However, standard Bayesian approaches typically require baseline hazard modeling…