Journal Articles (Refereed)
Akita, T., Tanaka, T., Imori, S. and Katanoda, K. (2025). Quantifying the Contribution of Viral Hepatitis Control Policies and Improvement of Hepatitis C Treatment in Japan during 2000-2030 based on Simulation Modelling. Asian Pacific Journal of Cancer Prevention, 26(10), 3513-3518. [URL]
Solvang, H. K., Imori, S., Biuw, M., Lindstrøm, U. and Haug, T. (2024). Categorical data analysis using discretization of continuous variables to investigate associations in marine ecosystems. Environmetrics, 35(6), e2867. [URL]
Fukutani, T., Yoshioka, Y., Imori, S., Yanagihara, H., Sumi, K., Myoken, Y., Fujita, Y. & Yanamoto, S. (2024). Efficacy of Episil® in patients with hematologic malignancies: a comparative study. BMC Oral Health, 24, 522. [URL]
Imori. S. (2023). Asymptotic optimality of Cp-type criteria in high-dimensional multivariate linear regression models. Statistica Sinica, 33, Online Special Issue "HIGH-DIMENSIONAL STATISTICS", 1233–1248. [URL]
Imori, S., von Rosen, D. & Oda, R. (2022). Growth Curve Model with Bilinear Random Coefficients. Sankhya A, 84, pages 477-508. [URL]
Sugiyama, T., Imori, S. & Tanaka, F. (2021). Self-consistent quantum tomography with regularization. Physical Review A, 103(6), 062615. [URL]
Imori, S. & von Rosen, D. (2020). Upper and Lower Bounds of the Dispersion of a Mean Estimator in the Growth Curve Model. Statistics and Applications, Special Issue "Challenges and Opportunities in Statistical Data Designing and Inference in the Emerging Global Scenario", 18(2), 35-44. [URL]
Imori, S. & von Rosen, D. (2020). On the mean and dispersion of the Moore-Penrose generalized inverse of a Wishart matrix. Electronic Journal of Linear Algebra, 36, 124-133. [URL]
Tanabe, R., Kamo, K., Fukui, K. & Imori, S. (2019). Statistical inference for estimating the incidence of cancer at the prefectural level in Japan. Japanese Journal of Clinical Oncology, 49(5), 481-485. [URL]
Imori, S. & Shimodaira, H. (2019). An information criterion for auxiliary variable selection in incomplete data analysis. Entropy, 21(3), 281. [URL]
Inatsu, Y. & Imori, S. (2018). Model selection criterion based on the prediction mean squared error in generalized estimating equations. Hiroshima Mathematical Journal, 48(3), 307-334. [URL]
Yanagihara, H., Kamo, K., Imori, S. & Yamamura, M. (2017). A study on the bias-correction effect of the AIC for selecting variables in normal multivariate linear regression models under model misspecification. REVSTAT–Statistical Journal, 15(3), 299-332. [URL]
Imori, S. (2015). Model selection criterion based on the multivariate quasi‐likelihood for generalized estimating equations. Scandinavian Journal of Statistics, 42(4), 1214-1224. [URL]
Imori, S. & von Rosen, D. (2015). Covariance components selection in high-dimensional growth curve model with random coefficients. Journal of Multivariate Analysis, 136, 86-94. [URL]
Imori, S. (2015). Consistent selection of working correlation structure in GEE analysis based on Stein’s loss function. Hiroshima Mathematical Journal, 45(1), 91-107. [URL]
Katayama, S. & Imori, S. (2014). Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis. Journal of Multivariate Analysis, 132, 138-150. [URL]
Imori, S., Yanagihara, H. & Wakaki, H. (2014). Simple formula for calculating bias‐corrected AIC in generalized linear models. Scandinavian Journal of Statistics, 41(2), 535-555. [URL]
Yanagihara, H., Kamo, K. I., Imori, S. & Satoh, K. (2012). Bias-corrected AIC for selecting variables in multinomial logistic regression models. Linear Algebra and its Applications, 436(11), 4329-4341. [URL]