Grouped generalized estimating equations for longitudinal data analysis
Tsubasa Ito, Shonosuke Sugasawa
Biometrics, Wiley, 02 Aug. 2022, [Peer-reviewed]
Scientific journal

Empirical best linear unbiased predictors in multivariate nested-error regression models
Tsubasa Ito, Tatsuya Kubokawa
Communications in Statistics - Theory and Methods, 50, 10, 2224, 2249, Informa UK Limited, 19 May 2021, [Peer-reviewed]
Scientific journal

Corrected empirical Bayes confidence region in a multivariate Fay–Herriot model
Tsubasa Ito, Tatsuya Kubokawa
Journal of Statistical Planning and Inference, 211, 12, 32, Elsevier BV, Mar. 2021, [Peer-reviewed]
Scientific journal

Excess Mortality From Suicide During the Early COVID-19 Pandemic Period in Japan: A Time-Series Modeling Before the Pandemic
Tatsuhiko Anzai, Keisuke Fukui, Tsubasa Ito, Yuri Ito, Kunihiko Takahashi
Journal of Epidemiology, 31, 2, 152, 156, Japan Epidemiological Association, 05 Feb. 2021, [Peer-reviewed]
Scientific journal

Improved confidence regions in meta-analysis of diagnostic test accuracy
Tsubasa Ito, Shonosuke Sugasawa
Computational Statistics and Data Analysis, 153, 107068, 107068, Elsevier BV, Jan. 2021, [Peer-reviewed]
Scientific journal

Robust estimation of mean squared error matrix of small area estimators in a multivariate Fay–Herriot model
Tsubasa Ito, Tatsuya Kubokawa
Japanese Journal of Statistics and Data Science, 3, 1, 39, 61, Springer Science and Business Media LLC, Jun. 2020, [Peer-reviewed]
Scientific journal

異質な集団が混在するデータに対する分位点回帰モデル
科学研究費助成事業 若手研究
Apr. 2022 - Mar. 2025
伊藤 翼
日本学術振興会, 若手研究, 北海道大学, 22K13375

High dimensional multivariate linear mixed model and application to small area estimation
Grants-in-Aid for Scientific Research
30 Aug. 2019 - 31 Mar. 2021
Ito Tsubasa
Multivariate small are estimation problem, especially the Fay-Herriot model in which we only obtain aggregated data for each area is considered in the setting where the ratio of the square of the dimension of the observed vectors and the number of areas converges to a constant. The convergence rates of the estimators of the parameters in the model are shown, noting that the dimensions of these parameters also increase in this setting. Small area mean vectors are predicted by the empirical best linear unbiased predictors, which can be obtained by substituting the estimators of the parameters into its bayes estimator. As its prediction risks, the mean squared error matrix and the confidence interval are constructed such that the approximation errors are of second order regarding the number of areas. It is shown that the additional terms appear in both compared with the results obtained when the dimension of the observed vectors is fixed.
Japan Society for the Promotion of Science, Grant-in-Aid for Research Activity Start-up, 19K23242