Sugasawa, Shonosuke

写真a

Affiliation

Faculty of Economics ( Mita )

Position

Professor

E-mail Address

E-mail address

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Papers 【 Display / hide 】

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

    Sugasawa S., Kim J.K., Morikawa K.

    Journal of Multivariate Analysis 214 2026.07

    ISSN  0047259X

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    This paper proposes a flexible Bayesian approach to multiple imputation based on conditional Gaussian mixture models. We introduce shrinkage priors for covariate-dependent mixing proportions, which allow the number of mixture components used for imputation to be selected automatically. We develop an efficient Markov chain Monte Carlo algorithm for posterior computation and imputation. The proposed method extends naturally to mixed data containing both continuous and discrete variables (e.g., binary and count outcomes). We also propose an approximate Bayesian inference procedure for parameters defined through loss functions, using the posterior predictive distribution of missing observations and extending bootstrap-based Bayesian inference for complete data. Numerical studies using simulated and real data demonstrate the proposed method.

  • The Group R2D2 Shrinkage Prior for Sparse Linear Models with Grouped Covariates

    Yanchenko E., Irie K., Sugasawa S.

    Statistics and Computing 36 ( 1 )  2026.02

    ISSN  09603174

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    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 grouped sparsity, where covariates exhibit some natural grouping structure. This paper proposes a novel extension of the R2-induced Dirichlet Decomposition (R2D2) prior to accommodate grouped variable selection in linear regression models. The proposed method, called the gR2D2 prior, employs a Dirichlet prior distribution on the coefficient of determination for each group, allowing for a flexible and adaptive shrinkage that operates at both group and individual variable levels. We present several theoretical properties of this proposed prior distribution while also developing a Markov Chain Monte Carlo algorithm. Through simulation studies and real-data analysis, we demonstrate that our method outperforms traditional shrinkage priors in terms of both estimation accuracy, inference, and prediction.

  • Similarity-based random partition distribution for clustering functional data

    Wakayama T., Sugasawa S., Kobayashi G.

    Journal of the Royal Statistical Society Series C Applied Statistics 75 ( 1 ) 100 - 119 2026.01

    ISSN  00359254

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    Random partition distribution is a crucial tool for model-based clustering. This study advances the field of random partition in the context of functional spatial data, focusing on the challenges posed by hourly population data across various regions and dates. We propose an extension of the generalized Dirichlet process, named the similarity-based generalized Dirichlet process (SGDP)-type distribution, to address the limitations of simple random partition distributions (e.g. those induced by the Dirichlet process), such as an overabundance of clusters. This model prevents excess cluster production and incorporates pairwise similarity information to ensure accurate and meaningful clustering. The theoretical properties of the SGDP-type distribution are studied. Then, SGDP-type random partition is applied to a real-world dataset of hourly population flow in 500 m meshes in the central part of Tokyo. In this empirical context, our method excels at detecting meaningful patterns in the data while accounting for spatial nuances. The results underscore the adaptability and utility of the method, showcasing its prowess in revealing intricate spatiotemporal dynamics. The proposed random partition will significantly contribute to urban planning, transportation, and policy-making and will be a helpful tool for understanding population dynamics and their implications.

  • Nonparametric Bayesian Adjustment of Unmeasured Confounders in Cox Proportional Hazards Models

    Orihara S., Sugasawa S., Ohigashi T., Hirano K., Nakagawa T., Taguri M.

    Statistics in Medicine 45 ( 1-2 )  2026.01

    ISSN  02776715

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    Unmeasured confounders pose a major challenge in accurately estimating causal effects in observational studies. To address this issue when estimating hazard ratios (HRs) using Cox proportional hazards models, several methods, including instrumental variables (IVs) approaches, have been proposed. However, these methods often face limitations, such as weak IV problems and restrictive assumptions regarding unmeasured confounder distributions. In this study, we introduce a novel nonparametric Bayesian procedure that provides accurate HR estimates while addressing these limitations. A key assumption of our approach is that unmeasured confounders exhibit a cluster structure. Under this assumption, we integrate two remarkable Bayesian techniques, the Dirichlet process mixture (DPM) and general Bayes (GB), to simultaneously (1) detect latent clusters based on the likelihood of exposure and outcome variables and (2) estimate HRs using the likelihood constructed within each cluster. Notably, leveraging DPM, our procedure eliminates the need for IVs by identifying unmeasured confounders under an alternative condition. Additionally, GB techniques remove the need for explicit modeling of the baseline hazard function, distinguishing our procedure from traditional Bayesian approaches. Simulation experiments demonstrate that the proposed Bayesian procedure outperforms existing methods in some performance metrics. Moreover, it achieves statistical efficiency comparable to the efficient estimator while accurately identifying cluster structures. These features highlight its ability to overcome challenges associated with traditional IV approaches for time-to-event data.

  • Causal Inference Under Threshold Manipulation: Bayesian Mixture Modeling and Heterogeneous Treatment Effects

    Kubota K., Sugasawa S.

    Proceedings of the Aaai Conference on Artificial Intelligence 40 ( 43 ) 36688 - 36695 2026

    ISSN  21595399

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    Many marketing applications, including credit card incentive programs, offer rewards to customers who exceed specific spending thresholds to encourage increased consumption. Quantifying the causal effect of these thresholds on customers is crucial for effective marketing strategy design. Although regression discontinuity design is a standard method for such causal inference tasks, its assumptions can be violated when customers, aware of the thresholds, strategically manipulate their spending to qualify for the rewards. To address this issue, we propose a novel framework for estimating the causal effect under threshold manipulation. The main idea is to model the observed spending distribution as a mixture of two distributions: one representing customers strategically affected by the threshold, and the other representing those unaffected. To fit the mixture model, we adopt a two-step Bayesian approach consisting of modeling nonbunching customers and fitting a mixture model to a sample around the threshold. We show posterior contraction of the resulting posterior distribution of the causal effect under large samples. Furthermore, we extend this framework to a hierarchical Bayesian setting to estimate heterogeneous causal effects across customer subgroups, allowing for stable inference even with small subgroup sample sizes. We demonstrate the effectiveness of our proposed methods through simulation studies and illustrate their practical implications using a real-world marketing dataset.

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Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • 複雑データに対するベイズモデリングの基盤創出

    2025.04
    -
    2030.03

    基盤研究(A), Principal investigator

  • General Bayesian approaches to econometric analysis

    2024.06
    -
    2027.03

    挑戦的研究（萌芽）, Principal investigator

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Courses Taught 【 Display / hide 】

  • RESEARCH SEMINAR (THESIS)

    2026

  • RESEARCH SEMINAR D

    2026

  • RESEARCH SEMINAR C

    2026

  • MATHEMATICAL STATISTICS

    2026

  • STATISTICS 1

    2026

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