Decision-guided perspectives on model uncertainty expand traditional statistical thinking
about managing, comparing, and combining inferences from sets of models. Bayesian …

The macroeconomy is a sophisticated dynamic system involving significant uncertainties
that complicate modelling. In response, decision makers consider multiple models that …

We discuss and develop Bayesian dynamic modelling and predictive decision synthesis for
portfolio analysis. The context involves model uncertainty with a set of candidate models for …

Decision-guided perspectives on model uncertainty expand traditional statistical thinking
about managing, comparing, and combining inferences from sets of models. In this …

We propose an ensemble framework for combining heterogeneous portfolio rules that
cannot be accommodated by previously proposed combination methods. Using our …

Bayesian predictive synthesis is useful in synthesizing multiple predictive distributions
coherently. However, the proof for the fundamental equation of the synthesized predictive …

We propose a novel time series forecasting method designed to handle vast sets of
predictive signals, many of which are irrelevant or short-lived. The method transforms …

This thesis studies prediction and decision-making with multiple models in a Bayesian
context. It is common practice to use multiple models in a forecasting and decision-making …

We discuss the finite sample theoretical properties of online predictions in non-stationary
time series under model misspecification. To analyze the theoretical predictive properties of …

抄録 ベータ・ガンマモデルとは, 各時点での周辺分布がガンマ分布となるようなマルコフ過程である.
本稿ではその性質と応用を概観する. ガンマ分布は正規分布の精度およびポアソン分布の平均の …