Research

Published and accepted papers

2024+

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

2. Imai, S., Koriyama, T., Yonekura, S., Sugasawa, S. and Nishiyama, Y. (2024+). Fully data-driven normalized and exponentiated Kernel density estimator with Hyvarinen score. Journal of Business and Economic Statistics, accepted.  (publication, arXiv)  

3. 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, accepted.  [open access]  (publication, arXiv)  

4. Mosaferi, S., Ghosh, M. and Sugasawa, S. (2024+). An unbiased predictor for skewed response variable with measurement error in covariate.  Statistica Sinica, accepted.  (publication, arXiv)

5. Hamura, Y., Irie, K. and Sugasawa, S. (2024+). Gibbs sampler for matrix generalized inverse Gaussian distributions.  Journal of Computational and Graphical Statistics, accepted.  (publication, arXiv, R-code)

6. 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)  

7. 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) 

8. 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)

9. 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)  

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

2023

11. 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)

12. 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)

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

14. 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)  

15. 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) 

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

17. 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)

18. 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)

2022

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

20. 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)  

21. 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)   

22. 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)

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

24. 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) 

25. 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) 

26. 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)    

27. 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)   

28. 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)    

29. 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)  

2021

30. 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) 

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

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

33. 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)

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

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

36. 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)

37. 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)

2020

38. 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)  

39. 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)   

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

41. 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)

42. 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)   

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

44. 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

45. 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)   

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

47. 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)

48. 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)

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

50. 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)

51. 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)

52. 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) 

53. 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)

54. 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)  

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

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

57. 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)

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

59. 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)   

60. 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)

61. 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.  (publication)

 (In Japanese)

• 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)