【概要】　【スライド】

（お問い合わせ）
経営管理大学院・経済学研究科 教授 チョルパン・アスリ
colpanasli[at]hotmail.com

経済学研究科・経営管理大学院 准教授 安達貴教
adachi.takanori.8m[at]kyoto-u.ac.jp

経済研究所　教授　原千秋
hara.chiaki.7x [at] kyoto-u.ac.jp

【論文】※4/14差替版

【論文】

要旨：Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to fully capture with a single model. Significant levels of model uncertainty– arising from these characteristics– cannot be resolved by model selection or simple ensemble methods, as performances are not homogeneous. We address this issue by proposing a novel methodology that captures spatially-varying model uncertainty, which we call Bayesian spatial predictive synthesis. Our proposal is defined by specifying a latent factor spatially-varying coefficient model as the synthesis function, which enables model coefficients to vary over the region to achieve flexible spatial model ensembling. We show that our proposal is derived from the theoretically best approximation of the data generating process and that it provides a finite sample theoretical guarantee for its predictive performance, specifically that the predictions are exact minimax. Two MCMC strategies are implemented for full uncertainty quantification, as well as a variational inference strategy for fast point inference. We also extend the estimation strategy for general responses. Through simulation examples and two real data applications, we demonstrate that our proposed Bayesian spatial predictive synthesis outperforms standard spatial models and ensemble methods, and advanced machine learning methods, in terms of predictive accuracy, while maintaining interpretability of the prediction mechanism.