2025+
Kubota, K., Sugasawa, S. Ochiai, K. and Hoshino, T. (2025+). Bayesian time-varying meta-analysis via hierarchical mean-variance random-effects models. Japanese Journal of Statistics and Data Science, accepted. [open access] (publication, arXiv)
Wakayama, T., Sugasawa, S. and Kobayashi, G. (2025+). Similarity-based random partition distribution for clustering functional data. Journal of the Royal Statistical Society: Series C, accepted. [open access] (publication, arXiv)
Hamura, Y., Irie, K. and Sugasawa, S. (2025+). Robust Bayesian modeling of counts with zero inflation and outliers: theoretical robustness and efficient computation. Journal of the American Statistical Association, accepted. [open access] (publication, arXiv, R-code)
Kurisu, D., Ishihara, T. and Sugasawa, S. (2025). Adaptively robust small area estimation: balancing robustness and efficiency of empirical Bayes confidence intervals. Scandinavian Journal of Statistics 52, 999-1017. [open access] (publication, arXiv, R-code)
Mosaferi, S., Ghosh, M. and Sugasawa, S. (2025). An unbiased predictor for skewed response variable with measurement error in covariate. Statistica Sinica 35, 1583-1604. (publication, arXiv)
Jin, Y., Wakayama, T., Jiang, R. and Sugasawa, S. (2025). Clustered factor analysis for multivariate spatial data. Spatial Statistics 66, 100889. [open access] (publication, arXiv)
Imai, S., Koriyama, T., Yonekura, S., Sugasawa, S. and Nishiyama, Y. (2025). Fully data-driven normalized and exponentiated kernel density estimator with Hyvarinen score. Journal of Business and Economic Statistics 43, 110-121. (publication, arXiv)
2020-2024
Li, H., Sugasawa, S. and Katayama, S. (2024). Adaptively robust and sparse K-means clustering. Transactions on Machine Learning Research. [open access] (publication, arXiv)
Kobayashi, G., Sugasawa, S., Kawakubo, Y., Han, D. and Choi, T. (2024). Predicting COVID-19 hospitalisation using a mixture of Bayesian predictive syntheses. Annals of Applied Statistics 18, 3383-3404. (publication, arXiv)
Wakayama, T. and Sugasawa, S. (2024). Spatiotemporal factor models for functional data with application to population map forecast. Spatial Statistics 62, 100849. [open access] (publication, arXiv)
Noma, H., Sugasawa, S. and Furukawa, T. A. (2024). Robust inference methods for meta-analysis involving influential outlying studies. Statistics in Medicine 43, 3778-3791. [open access] (publication, R-package)
Hamura, Y., Irie, K. and Sugasawa, S. (2024). Posterior robustness with milder conditions: contamination models revisited. Statistics and Probability Letters 210, 110130. (publication, arXiv)
Murakami, D., Sugasawa, S., Seya, H. and Griffith, D. (2024). Sub-model aggregation for scalable eigenvector spatial filtering: application to spatially varying coefficient modeling. Geographical Analysis 56, 768-798. [open access] (publication, arXiv)
Hamura, Y., Irie, K. and Sugasawa, S. (2024). Gibbs sampler for matrix generalized inverse Gaussian distributions. Journal of Computational and Graphical Statistics 33, 331-340. (publication, arXiv, R-code)
Onizuka, T., Hashimoto, S. and Sugasawa, S. (2024). Locally adaptive spatial quantile smoothing: application to monitoring crime density in Tokyo. Spatial Statistics 59, 100793. (publication, arXiv, R-code)
Onizuka, T., Hashimoto, S. and Sugasawa, S. (2024). Fast and locally adaptive Bayesian quantile regression using calibrated variational approximations. Statistics and Computing 34, article number: 15. (publication, arXiv, R-code)
Okano, R., Hamura, Y., Irie, K. and Sugasawa, S. (2024). Locally adaptive Bayesian isotonic regression with half shrinkage priors. Scandinavian Journal of Statistics 51, 109-141. (publication, arXiv, R-code)
Hamura, Y., Onizuka, T, Hashimoto, S. and Sugasawa, S. (2024). Sparse Bayesian inference on gamma-distributed observations using shape-scale inverse-gamma mixtures. Bayesian Analysis 19, 77-97. [open access] (publication, arXiv, R-code)
Wakayama, T. and Sugasawa, S. (2024). Functional horseshoe smoothing for functional trend estimation. Statistica Sinica 34, 1585-1602. (publication, arXiv)
Muto, S., Sugasawa, S. and Suzuki, M. (2023). Hedonic real estate price estimation with the spatiotemporal geostatistical model. Journal of Spatial Econometrics 4, article number: 10. [open access] (publication)
Noma, H., Hamura, Y., Sugasawa, S. and Furukawa, T. (2023). Improved methods to construct prediction intervals for network meta-analysis. Research Synthesis Methods 14, 794-806. (publication, R-package)
Wakayama, T. and Sugasawa, S. (2023). Trend filtering for functional data. Stat 12, e590. [open access] (publication, arXiv)
Yonekura, S. and Sugasawa, S. (2023). Adaptation of the tuning parameter in general Bayesian inference with robust divergence. Statistics and Computing 33, article number: 39. [open access] (publication, arXiv)
Hamura, Y., Irie, K. and Sugasawa, S. (2023). On data augmentation for models involving reciprocal gamma functions. Journal of Computational and Graphical Statistics 32, 908-916. [open access] (publication, arXiv, R-code)
Ito, T. and Sugasawa, S. (2023). Grouped generalized estimating equations for longitudinal data analysis. Biometrics 79, 1868-1879. (publication, arXiv, R-code)
Muto, S., Sugasawa, S. and Suzuki, M. (2023). Forecasting the housing vacancy rate in Japan using dynamic spatiotemporal effects models. Japanese Journal of Statistics and Data Science 6, 21-44. (publication)
Sugasawa, S., Nakagawa, T., Solvang, H. K., Subby, S. and Alrabeei, S. (2023). Dynamic spatio-temporal zero-inflated Poisson models for predicting Capelin distribution in the Barents sea. Japanese Journal of Statistics and Data Science 6, 1-20. (publication, arXiv)
Chaudhuri, S., Kubokawa, T. and Sugasawa, S. (2022). Covariance based moment equations for improved variance component estimation. Statistics 56, 1290-1318. (publication)
Sugasawa, S. and Kobayashi, G. (2022). Robust fitting of mixture models using weighted complete estimating equations. Computational Statistics & Data Analysis 174, 107526. (publication, arXiv, R-code)
Hamura, H., Irie, K. and Sugasawa, S. (2022). Log-regularly varying scale mixture of normals for robust regression. Computational Statistics & Data Analysis 173, 107517. (publication, arXiv, R-code)
Sugasawa, S. and Noma, H. (2022). Efficient testing and effect size estimation for set-based genetic association inference via semiparametric multilevel mixture modeling. Biometrical Journal 64, 1142-1152. (publication, arXiv)
Sugasawa, S. and Murakami, D. (2022). Adaptively Robust geographically weighted regression. Spatial Statistics 48, 100623. (publication, arXiv, R-code)
Nakagawa, M. and Sugasawa, S. (2022). Linguistic distance and economic prosperity: a cross-country analysis. Review of Development Economics 26, 793-834. [open access] (publication)
Kobayashi, G., Yamauchi, Y., Kakamu, K., Kawakubo, Y. and Sugasawa, S. (2022). Bayesian approach to Lorenz curve using time series grouped data. Journal of Business and Economic Statistics 40, 897-912. (publication, arXiv)
Hamura, H., Irie, K. and Sugasawa, S. (2022). On global-local shrinkage priors for count data. Bayesian Analysis 17, 545-564. [open access] (publication, arXiv, R-code)
Sugasawa, S., Morikawa, K. and Takahata, K. (2022). Bayesian semiparametric modeling of response mechanism for nonignorable missing data. TEST 31, 101-107. (publication, arXiv, R-code)
Saegusa, T., Sugasawa, S. and Lahiri, P. (2022). Parametric bootstrap confidence intervals for the multivariate Fay-Herriot model. Journal of Survey Statistics and Methodology 10, 115-130. (publication, arXiv)
Sugasawa, S. and Kim, J. K. (2022). An approximate Bayesian approach to model-assisted survey estimation with many auxiliary variables. Statistica Sinica 32, 1-22. (publication, arXiv, R-code)
Kubokawa, T., Sugasawa, S., Tamae, H. and Chaudhuri, S. (2021). General unbiased estimating equations for variance components in linear mixed models. Japanese Journal of Statistics and Data Science 4, 841-859. (publication, arXiv)
Sugasawa, S. and Yonekura, S. (2021). On selection criteria for the tuning parameter in robust divergence. Entropy 23, 1147. [open access] (publication, arXiv)
Sugasawa, S. and Murakami, D. (2021). Spatially clustered regression. Spatial Statistics 44, 100525. (publication, arXiv, R-code)
Sugasawa, S. and Hashimoto, S. (2021). Robust Bayesian changepoint analysis in the presence of outliers. Proceedings of the 13th KES-IDT Conference on Intelligent Decision Technologies, 469-478. (publication)
Sugasawa, S. (2021). Grouped heterogeneous mixture modeling for clustered data. Journal of the American Statistical Association 116, 999-1010. (publication, arXiv, R-code)
Sugasawa, S. and Noma, H. (2021). Efficient screening of predictive biomarkers for individual treatment selection. Biometrics 77, 249-257. (publication, arXiv, R-code)
Ito, T. and Sugasawa, S. (2021). Improved confidence regions in meta-analysis of diagnostic test accuracy. Computational Statistics & Data Analysis 153, 107068. (publication, arXiv, R-code)
Sugasawa, S. and Noma, H. (2021). A unified method for improved inference in random-effects meta-analysis. Biostatistics 22, 114-130. (publication, arXiv, R-code)
Kobayashi, G., Sugasawa, S., Tamae, H. and Ozu, T. (2020). Predicting intervention effect for COVID-19 in Japan: state space modeling approach. BioScience Trends 14, 174-181. (publication, arXiv)
Sugasawa, S. and Kubokawa, T. (2020). Small area estimation with mixed models: a review. Japanese Journal of Statistics and Data Science 3, 693-720. [open access] (publication)
Hashimoto, S. and Sugasawa, S. (2020). Robust Bayesian regression with synthetic posterior distributions. Entropy 22, 661. [open access] (publication, arXiv, R-code)
Sugasawa. S. (2020). Small area estimation of general parameters: Bayesian transformed spatial prediction approach. Japanese Journal of Statistics and Data Science 3, 167-181. (publication)
Sugasawa, S., Kobayashi, G. and Kawakubo, Y. (2020). Estimation and inference for area-wise spatial income distributions from grouped data. Computational Statistics & Data Analysis 145, 106904. (publication, arXiv, R-code)
Sugasawa, S. (2020). Robust empirical Bayes small area estimation with density power divergence. Biometrika 107, 467-480. (publication, arXiv)
Sugasawa, S., Kawakubo, Y. and Ogasawara, K. (2020). Small area estimation with spatially varying natural exponential families. Journal of Statistical Computation and Simulation 90, 1039-1056. (publication, arXiv)
2015-2019
Sugasawa, S. and Kubokawa, T. (2019). Adaptively transformed mixed model prediction of general finite population parameters. Scandinavian Journal of Statistics 46, 1025-1046. (publication, arXiv, R-code)
Sugasawa, S. and Noma, H. (2019). Estimating Individual treatment effects by gradient boosting trees. Statistics in Medicine 38, 5146-5159. (publication, R-code)
Sugasawa, S., Kubokawa, T. and Rao, J. N. K. (2019). Hierarchical Bayes small area estimation with an unknown link function. Scandinavian Journal of Statistics 46, 885-897. (publication, R-code)
Sugasawa, S., Kawakubo, Y. and Datta, G. S. (2019). Observed best selective prediction in small area estimation. Journal of Multivariate Analysis 173, 383-392. (publication)
Sugasawa, S., Kobayashi, G. and Kawakubo, Y. (2019). Latent mixture modeling for clustered data. Statistics and Computing 29, 537-548. (publication, arXiv)
Otani, T., Noma, H., Sugasawa, S., Kuchiba, A., Goto, A., Yamaji, T., Kochi, Y., Iwasaki, M., Matsui, S. and Tsunoda, T. (2019). Exploring predictive biomarkers from clinical genomics studies via multidimensional hierarchical mixture models for the development of molecular diagnostics. European Journal of Human Genetics 27, 140-149. (publication)
Kawakubo, Y., Sugasawa, S. and Kubokawa, T. (2018). Conditional Akaike information under covariate shift with application to small area estimation. Canadian Journal of Statistics 46, 316-335. (publication, arXiv)
Sugasawa, S., Kubokawa, T. and Rao, J. N. K. (2018). Small area estimation via unmatched sampling and linking models. TEST 27, 407-427. (publication, R-code)
Sugasawa, S., Noma, H., Otani, T., Nishino, J. and Matsui, S. (2017). An efficient and flexible test for rare variant effects. European Journal of Human Genetics 25, 752-757. (publication, R-code)
Sugasawa, S., Kubokawa, T. and Ogasawara, K. (2017). Empirical uncertain Bayes methods in area-level models. Scandinavian Journal of Statistics 44, 684-706. (publication, arXiv)
Sugasawa, S. and Kubokawa, T. (2017). Heteroscedastic nested error regression models with variance functions. Statistica Sinica 27, 1101-1123. (publication, arXiv)
Sugasawa, S. and Kubokawa, T. (2017). Transforming response values in small area prediction. Computational Statistics & Data Analysis 114, 47-60. (publication, arXiv)
Sugasawa, S., Tamae, H. and Kubokawa, T. (2017). Bayesian estimators for small area models shrinking both means and variances. Scandinavian Journal of Statistics 44, 150-167. (publication, arXiv, Correction)
Sugasawa, S. and Kubokawa, T. (2017). Bayesian estimators in uncertain nested error regression models. Journal of Multivariate Analysis 153, 52-63. (publication, arXiv)
Sugasawa, S. and Kubokawa, T. (2016). On conditional prediction errors in mixed models with application to small area estimation. Journal of Multivariate Analysis 148, 18-33. (publication, arXiv)
Kubokawa, T., Sugasawa, S., Ghosh, M. and Chaudhuri, S. (2016). Prediction in heteroscedastic nested error regression models with random dispersions. Statistica Sinica 26, 465-492. (publication, R-package)
Sugasawa, S. and Kubokawa, T. (2015). Parametric transformed Fay-Herriot model for small area estimation. Journal of Multivariate Analysis 139, 295-311. (publication, arXiv)
(Discussion)
Irie, K. and Sugasawa, S. (2021). Contributed discussion on "Multilevel linear models, Gibbs samplers and multigrid decomposition". Bayesian Analysis. [open access] (publication)
Sugasawa, S. (2025). Bayesian model synthesis for spatial prediction. Proceedings of the Institute of Statistical Mathematics 73, 53-63. (publication)
Sugasawa, S. (2024). Hierarchical Bayesian spatio-temporal modeling of zero-inflated count data. Mita Journal of Economics 117, 147-161. (publication)
Sugasawa, S. (2022). Grouped statistical modeling for heterogeneous data. Journal of the Japanese Statistical Society (Japanese Issue) 51, 295-317. (publication)
Otani, T., Sugasawa, S. and Noma, H. (2018). Aggregation-based association tests for identification of rare variants. Journal of the Japanese Society of Computational Statistics 31, 17-33. (publication)
Sugasawa, S. and Mochihashi, D. (2025). On prior distributions for orthogonal function sequences. (arXiv:2508.15552)
Li, H., Sugasawa, S. and Katayama, S. (2025). Robust global Frechet regression via weight regularization. (coming soon!)
Hamura, Y., Irie, K. and Sugasawa, S. (2025). Outlier-robust Bayesian multivariate analysis with correlation-intact sandwich mixture. (arXiv:2508.18004)
Sugasawa, S., Matsuda, T. and Nakagawa, T. (2025). Noise-robust phase connectivity estimation via Bayesian circular functional models. (coming soon!)
Momozaki, T., Sugasawa, S, Nakagawa, T., Solvang, H. K. and Subbey, S. (2025). Robust spatio-temporal distributional regression. (arXiv:2508.05041)
Takeishi, S. and Sugasawa, S. (2025). Scalable estimation of crossed random effects models via multi-way grouping. (arXiv:2507.15593)
Orihara, S., Momozaki, T. and Sugasawa, S. (2025). Bayesian doubly robust causal inference via posterior coupling. (arXiv:2506.04868)
Hamura, Y., Onizuka, T, Hashimoto, S. and Sugasawa, S. (2025). Robust Bayesian inference for censored survival models. (arXiv:2504.11147)
Yanchenko, E., Irie, K. and Sugasawa, S. (2024). The grouped R2D2 shrinkage prior for sparse linear models with grouped covariates. (arXiv:2412.15293)
Sugasawa, S. (2024). Prior sensitivity analysis without model re-fit. (arXiv:2409.19729)
Yamauchi, Y., Kobayashi, G. and Sugasawa, S. (2024). General Bayesian quantile regression for counts via generative modeling. (arXiv:2410.23081) R&R for Biometrics
Mosaferi, S. and Sugasawa, S. (2024). Bayesian estimation of variance under fine stratification via mean-variance smoothing. R&R for Journal of Survey Statistics and Methodology
Sugasawa, S. and Mochihashi, D. (2024). Spatially-dependent Indian buffet processes. (arXiv:2409.01943)
Babasaki, K., Sugasawa, S., McAlinn, K. and Takanashi, K. (2024). Ensemble doubly robust Bayesian inference via regression synthesis. (arXiv:2409.06288) R&R for Observational Studies
Wakayama, T. and Sugasawa, S. (2024). Ensemble prediction via covariate-dependent stacking. (arXiv:2408.09755) R&R for Statistics and Computing
Sugasawa, S., Hui, F. K. C. and Welsh, A. H. (2024). Robust linear mixed models using hierarchical gamma-divergence. (arXiv:2407.01883) R&R for Journal of Computational and Graphical Statistics
Sugasawa, S., Kobayashi, G. and Kawakubo, Y. (2024). Bayesian benchmarking small area estimation via entropic tilting. (arXiv:2407.17848) R&R for Journal of Survey Statistics and Methodology
Hiraki, D., Hamura, H., Irie, K. and Sugasawa, S. (2024). State-space modeling of shape-constrained functional time series. (arXiv:2404.07586)
Orihara, S., Sugasawa, S., Ohigashi, T., Nakagawa, T and Taguri, M. (2024). Nonparametric Bayesian adjustment of unmeasured confounders in Cox proportional hazards models. (arXiv:2312.02404) R&R for Statistics in Medicine
Momozaki, T., Nakagawa, T., Sugasawa, S. and Solvang, K. H. (2023). Semiparametric Copula estimation for spatially correlated multivariate mixed outcomes: analyzing visual sightings of fin whales from line transect survey. (arXiv:2312.12710)
McAlinn, K., Naghavi, A. J., Pignataro, G., Sugasawa, S. and Yamada, K. (2023). Patent waiver and incentives to innovate. (PsiArXiv)
Sugasawa, S., Ishihara, T. and Kurisu, D. (2023). Hierarchical regression discontinuity design: pursuing subgroup treatment effects. (arXiv:2309.01404, R-code) R&R for Journal of Causal Inference
Sugasawa, S., McAlinn, K., Takanashi, K. and Airoldi, E. A. (2023). Bayesian causal synthesis for meta-inference on heterogeneous treatment effect. (arXiv:2304.07726, R-code)
Sugasawa, S., Kim, J. K. and Morikawa, K. (2022). Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes. (arXiv:2208.07535)
Kobayashi, G., Sugasawa, S. and Kawakubo, Y. (2022). Spatio-temporal smoothing, interpolation and prediction of income distributions based on grouped data. (arXiv:2207.08384) R&R for Journal of the Royal Statistical Society: Series C
Cabel, D., Sugasawa, S., Kato, M., Takanashi, K. and McAlinn, K. (2022). Bayesian spatial predictive synthesis. (arXiv:2203.05197, R-code)
Hamura, H., Irie, K. and Sugasawa, S. (2020). Shrinkage with robustness: log-adjusted heavy-tailed priors. (arXiv:2001.08465, R-code) R&R for Bayesian Analysis