Research

Published and accepted papers

2025+

1. Wakayama, T. and Sugasawa, S. (2025+). Ensemble prediction via covariate-dependent stacking. Statistics and Computing, accepted. (arXiv) 

2. Sugasawa, S., Hui, F. K. C. and Welsh, A. H. (2025+). Robust linear mixed models using hierarchical gamma-divergence. Journal of Computational and Graphical Statistics, accepted.  (publication, arXiv)   

3. 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)  

4. 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)   

5. 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)  

6. 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) 

7. 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)

8. 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) 

9. 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

10. Li, H., Sugasawa, S. and Katayama, S. (2024). Adaptively robust and sparse K-means clustering. Transactions on Machine Learning Research.  [open access] (publication, arXiv)  

11. 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)  

12. 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) 

13. 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)   

14. Hamura, Y., Irie, K. and Sugasawa, S. (2024). Posterior robustness with milder conditions: contamination models revisited. Statistics and Probability Letters 210, 110130.  (publication, arXiv)  

15. 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)  

16. 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)

17. 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)  

18. 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) 

19. 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)

20. 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)  

21. Wakayama, T. and Sugasawa, S. (2024). Functional horseshoe smoothing for functional trend estimation. Statistica Sinica 34, 1585-1602. (publication, arXiv)  

22. 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)

23. 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)

24. Wakayama, T. and Sugasawa, S. (2023). Trend filtering for functional data. Stat 12, e590.  [open access] (publication, arXiv) 

25. 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)  

26. 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) 

27. Ito, T. and Sugasawa, S. (2023). Grouped generalized estimating equations for longitudinal data analysis. Biometrics 79, 1868-1879. (publication, arXiv, R-code) 

28. 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)

29. 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)

30. Chaudhuri, S., Kubokawa, T. and Sugasawa, S. (2022). Covariance based moment equations for improved variance component estimation.  Statistics 56, 1290-1318.  (publication)

31. 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)  

32. 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)   

33. 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)

34. Sugasawa, S. and Murakami, D. (2022). Adaptively Robust geographically weighted regression. Spatial Statistics 48, 100623.  (publication, arXiv, R-code)

35. 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) 

36. 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) 

37. 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)    

38. 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)   

39. 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)    

40. 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)  

41. 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) 

42. Sugasawa, S. and Yonekura, S. (2021). On selection criteria for the tuning parameter in robust divergence.  Entropy 23, 1147.  [open access]  (publication, arXiv)   

43. Sugasawa, S. and Murakami, D. (2021). Spatially clustered regression. Spatial Statistics 44, 100525.   (publication, arXiv, R-code)   

44. 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)

45. Sugasawa, S. (2021). Grouped heterogeneous mixture modeling for clustered data. Journal of the American Statistical Association 116, 999-1010.   (publication, arXiv, R-code)   

46. Sugasawa, S. and Noma, H. (2021). Efficient screening of predictive biomarkers for individual treatment selection. Biometrics 77, 249-257.   (publication, arXiv, R-code) 

47. 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)

48. 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)

49. 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)  

50. 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)   

51. Hashimoto, S. and Sugasawa, S. (2020). Robust Bayesian regression with synthetic posterior distributions. Entropy 22, 661. [open access]  (publication, arXiv, R-code) 

52. 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)

53. 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)   

54. Sugasawa, S. (2020). Robust empirical Bayes small area estimation with density power divergence. Biometrika 107, 467-480.   (publication, arXiv)       

55. 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

56. 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)   

57. Sugasawa, S. and Noma, H. (2019). Estimating Individual treatment effects by gradient boosting trees. Statistics in Medicine 38, 5146-5159.   (publication, R-code)

58. 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)

59. 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)

60. Sugasawa, S., Kobayashi, G. and Kawakubo, Y. (2019). Latent mixture modeling for clustered data. Statistics and Computing  29, 537-548.   (publication, arXiv)

61. 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)

62. 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)

63. 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) 

64. 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)

65. 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)  

66. Sugasawa, S. and Kubokawa, T. (2017). Heteroscedastic nested error regression models with variance functions. Statistica Sinica 27, 1101-1123.  (publication, arXiv)  

67. Sugasawa, S. and Kubokawa, T. (2017). Transforming response values in small area prediction. Computational Statistics & Data Analysis 114, 47-60.    (publication, arXiv)

68. 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)

69. Sugasawa, S. and Kubokawa, T. (2017). Bayesian estimators in uncertain nested error regression models. Journal of Multivariate Analysis 153, 52-63.  (publication, arXiv)

70. 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)   

71. 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)

72. 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)

 (In Japanese)

• 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)