Shonosuke Sugasawa

research

∙ 09/04/2023

Hierarchical Regression Discontinuity Design: Pursuing Subgroup Treatment Effects

Regression discontinuity design (RDD) is widely adopted for causal infer...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 08/21/2023

An Unbiased Predictor for Skewed Response Variable with Measurement Error in Covariate

We introduce a new small area predictor when the Fay-Herriot normal erro...

0 Sepideh Mosaferi, et al. ∙

research

∙ 08/11/2023

Clustering and Predicting Multiple Time Series Count Data via Mixture of Bayesian Predictive Syntheses: Analysis of COVID-19 Hospitalisation in Japan and Korea

Since the outbreak of COVID-19, governments and academia have made treme...

0 Genya Kobayashi, et al. ∙

research

∙ 08/03/2023

Similarity-based Random Partition Distribution for Clustering Functional Data

Random partitioned distribution is a powerful tool for model-based clust...

0 Tomoya Wakayama, et al. ∙

research

∙ 04/16/2023

Bayesian Causal Synthesis for Supra-Inference on Heterogeneous Treatment Effects

We propose a novel Bayesian methodology to mitigate misspecification and...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 03/01/2023

Posterior Robustness with Milder Conditions: Contamination Models Revisited

Robust Bayesian linear regression is a classical but essential statistic...

0 Yasuyuki Hamura, et al. ∙

research

∙ 02/20/2023

Gibbs Sampler for Matrix Generalized Inverse Gaussian Distributions

Sampling from matrix generalized inverse Gaussian (MGIG) distributions i...

0 Yasuyuki Hamura, et al. ∙

research

∙ 02/09/2023

Spatiotemporal factor models for functional data with application to population map forecast

With the proliferation of mobile devices, an increasing amount of popula...

0 Tomoya Wakayama, et al. ∙

research

∙ 12/02/2022

Fully Data-driven Normalized and Exponentiated Kernel Density Estimator with Hyvärinen Score

We introduce a new deal of kernel density estimation using an exponentia...

0 Shunsuke Imai, et al. ∙

research

∙ 11/09/2022

Fast and Locally Adaptive Bayesian Quantile Smoothing using Calibrated Variational Approximations

Quantiles are useful characteristics of random variables that can provid...

0 Takahiro Onizuka, et al. ∙

research

∙ 08/16/2022

Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes

This paper proposes a flexible Bayesian approach to multiple imputation ...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 08/10/2022

Locally Adaptive Bayesian Isotonic Regression using Half Shrinkage Priors

Isotonic regression or monotone function estimation is a problem of esti...

0 Ryo Okano, et al. ∙

research

∙ 07/18/2022

Spatio-temporal smoothing, interpolation and prediction of income distributions based on grouped data

In Japan, the Housing and Land Survey (HLS) provides grouped data on hou...

0 Genya Kobayashi, et al. ∙

research

∙ 04/21/2022

Functional Horseshoe Smoothing for Functional Trend Estimation

Due to developments in instruments and computers, functional observation...

0 Tomoya Wakayama, et al. ∙

research

∙ 03/16/2022

Sparse Bayesian Inference on Positive-valued Data using Global-local Shrinkage Priors

In various applications, we deal with high-dimensional positive-valued d...

0 Yasuyuki Hamura, et al. ∙

research

∙ 03/10/2022

Spatially-Varying Bayesian Predictive Synthesis for Flexible and Interpretable Spatial Prediction

Spatial data are characterized by their spatial dependence, which is oft...

3 Danielle Cabel, et al. ∙

research

∙ 03/03/2022

On Data Augmentation for Models Involving Reciprocal Gamma Functions

In this paper, we introduce a new and efficient data augmentation approa...

0 Yasuyuki Hamura, et al. ∙

research

∙ 02/19/2022

Bayesian Quantile Trend Filtering on Graphs using Shrinkage Priors

Quantiles are useful characteristics of random variables that can provid...

0 Takahiro Onizuka, et al. ∙

research

∙ 11/01/2021

Spatio-temporal Modeling of Zero-Inflated Count: Efficient Posterior Computation and Application to Monitoring Capelin Distribution in the Barents Sea

We consider a model for predicting the spatio-temporal distribution of a...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 08/26/2021

Robust and Efficient Empirical Bayes Confidence Intervals using γ-Divergence

Although parametric empirical Bayes confidence intervals of multiple nor...

0 Daisuke Kurisu, et al. ∙

research

∙ 06/30/2021

Adaptively Robust Geographically Weighted Regression

We develop a new robust geographically weighted regression method in the...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 06/22/2021

On Selection Criteria for the Tuning Parameter in Robust Divergence

While robust divergence such as density power divergence and γ-divergenc...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 06/19/2021

Robust Hierarchical Modeling of Counts under Zero-inflation and Outliers

Count data with zero inflation and large outliers are ubiquitous in many...

0 Yasuyuki Hamura, et al. ∙

research

∙ 06/13/2021

Adaptation of the Tuning Parameter in General Bayesian Inference with Robust Divergence

We introduce a methodology for robust Bayesian estimation with robust di...

0 Shouto Yonekura, et al. ∙

research

∙ 05/17/2021

General Unbiased Estimating Equations for Variance Components in Linear Mixed Models

This paper introduces a general framework for estimating variance compon...

0 Tatsuya Kubokawa, et al. ∙

research

∙ 04/06/2021

Locally Adaptive Smoothing for Functional Data

Despite increasing accessibility to function data, effective methods for...

0 Tomoya Wakayama, et al. ∙

research

∙ 11/03/2020

Spatially Clustered Regression

Spatial regression or geographically weighted regression models have bee...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 06/26/2020

Parametric Bootstrap Confidence Intervals for the Multivariate Fay-Herriot Model

The multivariate Fay-Herriot model is quite effective in combining infor...

0 Takumi Saegusa, et al. ∙

research

∙ 06/11/2020

Grouped GEE Analysis for Longitudinal Data

Generalized estimating equation (GEE) is widely adopted for regression m...

0 Tsubasa Ito, et al. ∙

research

∙ 05/06/2020

Log-Regularly Varying Scale Mixture of Normals for Robust Regression

Linear regression with the classical normality assumption for the error ...

0 Yasuyuki Hamura, et al. ∙

research

∙ 04/28/2020

Predicting Infection of COVID-19 in Japan: State Space Modeling Approach

The number of confirmed cases of the coronavirus disease (COVID-19) in J...

0 Genya Kobayashi, et al. ∙

research

∙ 04/08/2020

Robust Mixture Modeling using Weighted Complete Estimating Equations

Mixture modeling that takes account of potential heterogeneity in data i...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 03/12/2020

Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling: Application to a genome-wide associatio

In genetic association studies, rare variants with extremely small allel...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 01/23/2020

Shrinkage with Robustness: Log-Adjusted Priors for Sparse Signals

We introduce a new class of distributions named log-adjusted shrinkage p...

0 Yasuyuki Hamura, et al. ∙

research

∙ 10/02/2019

Robust Bayesian Regression with Synthetic Posterior

Regression models are fundamental tools in statistics, but they typicall...

0 Shintaro Hashimoto, et al. ∙

research

∙ 09/06/2019

Bayesian Semiparametric Estimation with Nonignorable Nonresponse

Statistical inference with nonresponse is quite challenging, especially ...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 08/19/2019

Bayesian approach to Lorenz curve using time series grouped data

This study is concerned with estimating the inequality measures associat...

0 Genya Kobayashi, et al. ∙

research

∙ 07/02/2019

On Global-local Shrinkage Priors for Count Data

Global-local shrinkage prior has been recognized as useful class of prio...

0 Yasuyuki Hamura, et al. ∙

research

∙ 06/20/2019

Improving the Accuracy of Confidence Intervals and Regions in Multivariate Random-effects Meta-analysis

Multivariate random-effects meta-analyses have been widely applied in ev...

0 Tsubasa Ito, et al. ∙

research

∙ 06/11/2019

An approximate Bayesian approach to regression estimation with many auxiliary variables

Model-assisted estimation with complex survey data is an important pract...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 05/05/2019

Efficient selection of predictive biomarkers for individual treatment selection

The development of molecular diagnostic tools to achieve individualized ...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 04/25/2019

Estimation and inference for area-wise spatial income distributions from grouped data

Estimating income distributions plays an important role in the measureme...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 04/03/2018

Grouped Heterogeneous Mixture Modeling for Clustered Data

Clustered data which has a grouping structure (e.g. postal area, school,...

0 Shonosuke Sugasawa, et al. ∙

research

∙ 11/17/2017

A Unified Method for Exact Inference in Random-effects Meta-analysis via Monte Carlo Conditioning

Random-effects meta-analyses have been widely applied in evidence synthe...

0 Shonosuke Sugasawa, et al. ∙

Shonosuke Sugasawa

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