| Predicting intervention effect for COVID-19 in Japan: state space modeling approach G Kobayashi, S Sugasawa, H Tamae, T Ozu BioScience Trends 14 (3), 174-181, 2020 | 51 | 2020 |
| Bayesian estimators for small area models shrinking both means and variances S Sugasawa, H Tamae, T Kubokawa Scandinavian Journal of Statistics 44 (1), 150-167, 2017 | 42 | 2017 |
| Transforming response values in small area prediction S Sugasawa, T Kubokawa Computational Statistics & Data Analysis 114, 47-60, 2017 | 33 | 2017 |
| Small area estimation with mixed models: a review S Sugasawa, T Kubokawa Japanese Journal of Statistics and Data Science 3, 693–720, 2020 | 22 | 2020 |
| Parametric transformed Fay–Herriot model for small area estimation S Sugasawa, T Kubokawa Journal of Multivariate Analysis 139, 295-311, 2015 | 21 | 2015 |
| Adaptively transformed mixed‐model prediction of general finite‐population parameters S Sugasawa, T Kubokawa Scandinavian Journal of Statistics 46 (4), 1025-1046, 2019 | 19* | 2019 |
| Estimating individual treatment effects by gradient boosting trees S Sugasawa, H Noma Statistics in medicine 38 (26), 5146-5159, 2019 | 18 | 2019 |
| Spatially clustered regression S Sugasawa, D Murakami Spatial Statistics 44, 100525, 2021 | 15 | 2021 |
| Log-regularly varying scale mixture of normals for robust regression Y Hamura, K Irie, S Sugasawa Computational Statistics & Data Analysis 173, 107517, 2022 | 14 | 2022 |
| Adaptation of the tuning parameter in general Bayesian inference with robust divergence S Yonekura, S Sugasawa Statistics and Computing 33 (2), 39, 2023 | 13 | 2023 |
| On global-local shrinkage priors for count data Y Hamura, K Irie, S Sugasawa Bayesian Analysis 17 (2), 545-564, 2022 | 13 | 2022 |
| Grouped heterogeneous mixture modeling for clustered data S Sugasawa Journal of the American Statistical Association 116 (534), 999-1010, 2021 | 13 | 2021 |
| A unified method for improved inference in random effects meta-analysis S Sugasawa, H Noma Biostatistics 22 (1), 114-130, 2021 | 11 | 2021 |
| Robust Bayesian regression with synthetic posterior distributions S Hashimoto, S Sugasawa Entropy 22 (6), 661, 2020 | 11 | 2020 |
| Small area estimation via unmatched sampling and linking models S Sugasawa, T Kubokawa, JNK Rao Test 27, 407-427, 2018 | 10 | 2018 |
| Prediction in heteroscedastic nested error regression models with random dispersions T Kubokawa, S Sugasawa, M Ghosh, S Chaudhuri Statistica Sinica, 465-492, 2016 | 10 | 2016 |
| On selection criteria for the tuning parameter in robust divergence S Sugasawa, S Yonekura Entropy 23 (9), 1147, 2021 | 9 | 2021 |
| Robust empirical Bayes small area estimation with density power divergence S Sugasawa Biometrika 107 (2), 467-480, 2020 | 9 | 2020 |
| Trend filtering for functional data T Wakayama, S Sugasawa Stat 12 (1), e590, 2023 | 8* | 2023 |
| Robust hierarchical modeling of counts under zero-inflation and outliers Y Hamura, K Irie, S Sugasawa arXiv preprint arXiv:2106.10503, 2021 | 8 | 2021 |
Hisashi NomaThe Institute of Statistical Mathematics確認したメール アドレス: ism.ac.jp
