Search Results for author: Kenichiro McAlinn

Found 9 papers, 0 papers with code

Bayesian Spatial Predictive Synthesis

no code implementations10 Mar 2022 Danielle Cabel, Shonosuke Sugasawa, Masahiro Kato, Kosaku Takanashi, Kenichiro McAlinn

Spatial data are characterized by their spatial dependence, which is often complex, non-linear, and difficult to capture with a single model.

Model Selection Uncertainty Quantification +1

Policy Choice and Best Arm Identification: Asymptotic Analysis of Exploration Sampling

no code implementations16 Sep 2021 Kaito Ariu, Masahiro Kato, Junpei Komiyama, Kenichiro McAlinn, Chao Qin

We consider the "policy choice" problem -- otherwise known as best arm identification in the bandit literature -- proposed by Kasy and Sautmann (2021) for adaptive experimental design.

Decision Making Experimental Design

Learning Causal Models from Conditional Moment Restrictions by Importance Weighting

no code implementations3 Aug 2021 Masahiro Kato, Masaaki Imaizumi, Kenichiro McAlinn, Haruo Kakehi, Shota Yasui

To address this issue, we propose a method that transforms conditional moment restrictions to unconditional moment restrictions through importance weighting, using a conditional density ratio estimator.

Causal Inference

Convergence of Computed Dynamic Models with Unbounded Shock

no code implementations11 Mar 2021 Kenichiro McAlinn, Kosaku Takanashi

In this regard, Fernandez-Villaverde, Rubio-Ramirez, and Santos (2006) show convergence of the likelihood, when the shock has compact support.

Controlling False Discovery Rates under Cross-Sectional Correlations

no code implementations15 Feb 2021 Junpei Komiyama, Masaya Abe, Kei Nakagawa, Kenichiro McAlinn

We achieve superior statistical power to existing methods and prove that the false discovery rate is controlled.

Time Series Time Series Analysis

Equivariant online predictions of non-stationary time series

no code implementations20 Nov 2019 Kōsaku Takanashi, Kenichiro McAlinn

To analyze the theoretical predictive properties of statistical methods under this setting, we first define the Kullback-Leibler risk, in order to place the problem within a decision theoretic framework.

Epidemiology Time Series +1

Cannot find the paper you are looking for? You can Submit a new open access paper.