21 citations · 90 across the 97 of their papers we have counts for
13 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…
Information Gap and Feasibility-Aware Inference in Binomial Logistic Mixtures
Yuta Hayashida, Shonosuke Sugasawa
This paper studies the information gap between mixture detection and label recovery in binomial logistic mixtures. Standard likelihood-based criteria such as the Bayesian informati…
Causal Small Area Estimation with Survey-only Covariates
Tsubasa Ito, Shonosuke Sugasawa
Area-specific causal inference is important in many policy and survey applications, where the goal is to evaluate treatment effects for small geographic or demographic domains. Exi…
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…