21 citations · 90 across the 97 of their papers we have counts for
92 papers · 1 filter
Beyond Tweedie's Formula: Conditional Score Modeling for Empirical Bayes Inference
Shonosuke Sugasawa, Zhigen Zhao
We propose conditional f-modeling (Cf-modeling), a framework for empirical Bayes inference with covariates. A central identity shows that the conditional marginal score function de…
Finite Mixtures of Generalized Estimating Equations for Clustering Multivariate Correlated Outcomes
Shonosuke Sugasawa, Francis K. C. Hui
Multivariate correlated outcomes occur across disciplines, including ecology, social sciences, and psychometrics. This paper focuses on clustering these outcomes across observation…
Log-regularly varying scale mixture of asymmetric Laplaces for robust Bayesian quantile regression
Dongu Han, Genya Kobayashi, Shonosuke Sugasawa
Bayesian quantile regression based on the asymmetric Laplace (AL) distribution can be sensitive to extreme observations because of its exponentially decaying tails. We propose a ro…
Efficient Bayesian Inference in the Cox Model via Rank-Ordered Likelihood
Tomohiro Ohigashi, Shunichiro Orihara, Shonosuke Sugasawa
In Bayesian inference for the Cox proportional hazards model, modeling the baseline hazard function is challenging. Recently, direct Bayesian inference using the partial likelihood…
Dynamic Bayesian regression quantile synthesis for forecasting outlook-at-risk
Genya Kobayashi, Shonosuke Sugasawa, Yuta Yamauchi +1
This paper proposes dynamic Bayesian regression quantile synthesis (DRQS), a novel method for quantile forecasting within the Bayesian predictive synthesis (BPS) framework designed…
Direct Bayesian Additive Regression Trees for Conditional Average Treatment Effects in Regression Discontinuity Designs
Daisuke Kondo, Shonosuke Sugasawa
Regression discontinuity designs (RDD) are widely used for causal inference. In many empirical applications, treatment effects vary substantially with covariates, and ignoring such…
Bayesian Estimation of Variance under Fine Stratification via Mean-Variance Smoothing
Sepideh Mosaferi, Shonosuke Sugasawa
Fine stratification survey is useful in many applications as its point estimator is unbiased, but the variance estimator under the design cannot be easily obtained, particularly wh…
Contrastive Bayesian Inference for Unnormalized Models
Naruki Sonobe, Shonosuke Sugasawa, Daichi Mochihashi +1
Unnormalized (or energy-based) models provide a flexible framework for capturing the characteristics of data with complex dependency structures. However, the application of standar…
Tree-Embedded Bayesian Factor Models for Multidimensional Categorical Distributions
Naoki Awaya, Keisuke Sasaki, Genya Kobayashi +1
Analyzing data collected from multiple observational units to estimate common and heterogeneous structures through a hierarchical model is a central task in Bayesian inference, and…
Difference-in-Differences under Local Dependence on Networks
Akihiro Sato, Shonosuke Sugasawa
Estimating causal effects under interference, where the stable unit treatment value assumption is violated, is critical in fields such as regional and public economics. Much of the…
The Covariate-Assisted Bayesian Intransitive Bradley-Terry Model via Combinatorial Hodge Theory
Hisaya Okahara, Tomoyuki Nakagawa, Shonosuke Sugasawa
Pairwise comparison data are widely used to recover latent rankings, yet the models in dominant use assume stochastic transitivity. When preferences are in fact intransitive, a sin…
Repulsive g-Priors for Regression Mixtures
Yuta Hayashida, Shonosuke Sugasawa
Mixture regression models are powerful tools for capturing heterogeneous covariate-response relationships, yet classical finite mixtures and Bayesian nonparametric alternatives oft…
Propensity Patchwork Kriging for Scalable Inference on Heterogeneous Treatment Effects
Hajime Ogawa, Shonosuke Sugasawa
Gaussian process-based models are attractive for estimating heterogeneous treatment effects (HTE), but their computational cost limits scalability in causal inference settings. In…
On Misspecified Error Distributions in Bayesian Functional Clustering: Consequences and Remedies
Fumiya Iwashige, Tomoya Wakayama, Shonosuke Sugasawa +1
Nonparametric Bayesian approaches provide a flexible framework for clustering without pre-specifying the number of groups, yet they are well known to overestimate the number of clu…
Efficient Prior Sensitivity and Tipping-point Analysis for Medical Research: Revisiting Sampling Importance Resampling
Tomohiro Ohigashi, Shonosuke Sugasawa
Bayesian methods have received increasing attention in medical research, where sensitivity analysis of prior distributions is essential. Such analyses typically require the evaluat…
Noise-Robust Phase Connectivity Estimation via Bayesian Circular Functional Models
Shonosuke Sugasawa, Takeru Matsuda, Tomoyuki Nagakawa
The phase locking value (PLV) is a widely used measure to detect phase connectivity. Main drawbacks of the standard PLV are it can be sensitive to noisy observations and does not p…
Causal Inference under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects
Kohsuke Kubota, Shonosuke Sugasawa
Many marketing applications, including credit card incentive programs, offer rewards to customers who exceed specific spending thresholds to encourage increased consumption. Quanti…
Robust Spatio-Temporal Distributional Regression
Tomotaka Momozaki, Shonosuke Sugasawa, Tomoyuki Nakagawa +2
Motivated by investigating spatio-temporal patterns of the distribution of continuous variables, we consider describing the conditional distribution function of the response variab…
Outlier-Robust Bayesian Multivariate Analysis with Correlation-Intact Sandwich Mixture
Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa
Handling outliers is a fundamental challenge in multivariate data analysis because outliers may distort the structures of correlation or conditional independence. Although robust B…
Sequential Adaptive Priors for Orthogonal Functions
Shonosuke Sugasawa, Daichi Mochihashi
We propose a novel class of prior distributions for sequences of orthogonal functions, which are frequently required in various statistical models such as functional principal comp…
Scalable Estimation of Crossed Random Effects Models via Multi-way Discretization
Shota Takeishi, Shonosuke Sugasawa
Cross-classified data frequently arise in scientific fields such as education, healthcare, and social sciences. A common modeling strategy is to introduce crossed random effects wi…
Bayesian Doubly Robust Causal Inference via Posterior Coupling
Shunichiro Orihara, Tomotaka Momozaki, Shonosuke Sugasawa
Bayesian doubly robust (DR) causal inference faces a fundamental dilemma: joint modeling of outcome and propensity score suffers from the feedback problem where outcome information…
Robust Bayesian Inference for Censored Survival Models
Yasuyuki Hamura, Takahiro Onizuka, Shintaro Hashimoto +1
This paper proposes a robust Bayesian accelerated failure time model for censored survival data. We develop a new family of life-time distributions using a scale mixture of the gen…
Bayesian Time-Varying Meta-Analysis via Hierarchical Mean-Variance Random-effects Models
Kohsuke Kubota, Shonosuke Sugasawa, Keiichi Ochiai +1
Meta-analysis is widely used to integrate results from multiple experiments to obtain generalized insights. Since meta-analysis datasets are often heteroscedastic due to varying su…