21 citations · 90 across the 97 of their papers we have counts for
12 papers · 1 filter
The Group R2D2 Shrinkage Prior for Sparse Linear Models with Grouped Covariates
Eric Yanchenko, Kaoru Irie, Shonosuke Sugasawa
Shrinkage priors are a popular Bayesian paradigm to handle sparsity in high-dimensional regression. Still limited, however, is a flexible class of shrinkage priors to handle groupe…
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…
Ensemble Doubly Robust Bayesian Inference via Regression Synthesis
Kaoru Babasaki, Shonosuke Sugasawa, Kosaku Takanashi +1
The doubly robust estimator, which models both the propensity score and outcomes, is a popular approach to estimate the average treatment effect in the potential outcome setting. T…
Spatially Dependent Indian Buffet Processes
Shonosuke Sugasawa, Daichi Mochihashi
We develop a new stochastic process called spatially dependent Indian buffet processes (sIBP) for binary feature matrices of unbounded columns with spatial correlations between sub…
Prior Sensitivity Analysis without Model Re-fit
Shonosuke Sugasawa
Prior sensitivity analysis is a fundamental method to check the effects of prior distributions on the posterior distribution in Bayesian inference. Exploring the posteriors under s…
Clustered Factor Analysis for Multivariate Spatial Data
Yanxiu Jin, Tomoya Wakayama, Renhe Jiang +1
Factor analysis has been extensively used to reveal the dependence structures among multivariate variables, offering valuable insight in various fields. However, it cannot incorpor…
Ensemble Prediction via Covariate-dependent Stacking
Tomoya Wakayama, Shonosuke Sugasawa
This study proposes a novel approach to ensemble prediction, called "covariate-dependent stacking" (CDST). Unlike traditional stacking and model averaging methods, CDST allows mode…
Bayesian Benchmarking Small Area Estimation via Entropic Tilting
Shonosuke Sugasawa, Genya Kobayashi, Yuki Kawakubo
Benchmarking estimation and its risk evaluation is a practically important issue in small area estimation. While Bayesian methods have been widely adopted in small area estimation,…
Adaptively Robust and Sparse K-means Clustering
Hao Li, Shonosuke Sugasawa, Shota Katayama
While K-means is known to be a standard clustering algorithm, its performance may be compromised due to the presence of outliers and high-dimensional noisy variables. This paper pr…
Robust Linear Mixed Models using Hierarchical Gamma-Divergence
Shonosuke Sugasawa, Francis K. C. Hui, Alan H. Welsh
Linear mixed models (LMMs) are a popular class of methods for analyzing longitudinal and clustered data. However, such models can be sensitive to outliers, and this can lead to bia…
State-Space Modeling of Shape-constrained Functional Time Series
Daichi Hiraki, Yasuyuki Hamura, Kaoru Irie +1
Functional time series data frequently appears in econometric analyses, where the functions of interest are subject to some shape constraints, including monotonicity and convexity,…
Sub-model aggregation for scalable eigenvector spatial filtering: Application to spatially varying coefficient modeling
Daisuke Murakami, Shonosuke Sugasawa, Hajime Seya +1
This study proposes a method for aggregating/synthesizing global and local sub-models for fast and flexible spatial regression modeling. Eigenvector spatial filtering (ESF) was use…