3 papers
stat.ME2026
Dynamic Bayesian regression quantile synthesis for forecasting outlook-at-risk
Genya Kobayashi, Shonosuke Sugasawa, Yuta Yamauchi +1
This paper proposes dynamic Bayesian regression quantile synthesis (DRQS), a novel method for quantile forecasting within the Bayesian predictive synthesis (BPS) framework designed…
stat.ME2025
General Bayesian quantile regression for counts via generative modeling
Yuta Yamauchi, Genya Kobayashi, Shonosuke Sugasawa
Count data frequently arises in biomedical applications, such as the length of hospital stay. However, their discrete nature poses significant challenges for appropriately modeling…
stat.ME2024
Bayesian factor zero-inflated Poisson model for multiple grouped count data
Genya Kobayashi, Yuta Yamauchi
This paper proposes a computationally efficient Bayesian factor model for multiple grouped count data. Adopting the link function approach, the proposed model can capture the assoc…