| 1. |
Hirose M.Y., Ghosh M. and Ghosh T., Arc-Sin Transformation for Binomial Sample Proportions in Small Area Estimation, Statistica Sinica, 33, 705-727, 2023.04. |
| 2. |
Malay Ghosh, Tamal Ghosh, Masayo Y. Hirose, Poisson Counts, Square Root Transformation and Small Area Estimation: Square Root Transformation, Sankhya B, 10.1007/s13571-021-00269-8, 84, 2, 449-471, 2022.11, The paper intends to serve two objectives. First, it revisits the celebrated Fay-Herriot model, but with homoscedastic known error variance. The motivation comes from an analysis of count data, in the present case, COVID-19 fatality for all counties in Florida. The Poisson model seems appropriate here, as is typical for rare events. An empirical Bayes (EB) approach is taken for estimation. However, unlike the conventional conjugate gamma or the log-normal prior for the Poisson mean, here we make a square root transformation of the original Poisson data, along with square root transformation of the corresponding mean. Proper back transformation is used to infer about the original Poisson means. The square root transformation makes the normal approximation of the transformed data more justifiable with added homoscedasticity. We obtain exact analytical formulas for the bias and mean squared error of the proposed EB estimators. In addition to illustrating our method with the COVID-19 example, we also evaluate performance of our procedure with simulated data as well.. |
| 3. |
Masayo Y. Hirose, Partha Lahiri, Multi-Goal Prior Selection: A Way to Reconcile Bayesian and Classical Approaches for Random Effects Models, Journal of the American Statistical Association, 2021.04, (Ref. https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1737532). |
| 4. |
Masayo Y. Hirose, A class of general adjusted maximum likelihood methods for desirable mean squared error estimation of EBLUP under the Fay–Herriot small area model, Journal of Statistical Planning and Inference, 199, 302-310, 2019.03. |
| 5. |
Masayo Yoshimori Hirose, Partha Lahiri, Estimating variance of random effects to solve multiple problems simultaneously, The Annals of Statistics, 10.1214/17-AOS1600, 46, 4, 1721-1741, 2018.08. |
| 6. |
Masayo Y. Hirose, Partha Lahiri, ESTIMATING VARIANCE OF RANDOM EFFECTS TO SOLVE MULTIPLE PROBLEMS SIMULTANEOUSLY, Annals of Statistics, 46, 4, 1721-1741, 2018.08. |
| 7. |
Masayo Yoshimori Hirose, Non-area-specific adjustment factor for second-order efficient empirical Bayes confidence interval, Computational Statistics and Data Analysis, 116, 67-78, 2017.12. |
| 8. |
Dan Takeuchi, Anusak Kerdsin, Yukihiro Akeda, Piphat Chiranairadul, Phacharaphan Loetthong, Nutchada Tanburawong, Prasanee Areeratana, Panarat Puangmali, Kasean Khamisara, Wirasinee Pinyo, Rapeepun Anukul, Sutit Samerchea, Punpong Lekhalula, Tatsuya Nakayama, Kouji Yamamoto, Masayo Hirose, Shigeyuki Hamada, Surang Dejsirilert, Kazunori Oishi, Impact of a food safety campaign on streptococcus suis infection in humans in Thailand, American Journal of Tropical Medicine and Hygiene, 96, 6, 1370-1377, 2017.01. |
| 9. |
Masayo Yoshimori, Numerical comparison between different empirical prediction intervals under the fay-herriot model, Communications in Statistics: Simulation and Computation, 10.1080/03610918.2013.809102, 44, 5, 1158-1170, 2015.05, Recently, an empirical best linear unbiased predictor is widely used as a practical approach to small area inference. It is also of interest to construct empirical prediction intervals. However, we do not know which method should be used from among the several existing prediction intervals. In this article, we first obtain an empirical prediction interval by using the residual maximum likelihood method for estimating unknown model variance parameters. Then we compare the later with other intervals with the residual maximum likelihood method. Additionally, some different parametric bootstrap methods for constructing empirical prediction intervals are also compared in a simulation study.. |
| 10. |
Masayo Yoshimori, Partha Lahiri, A second-order efficient empirical bayes confidence interval, The Annals of Statistics, 42, 4, 1-29, 2014.08. |
| 11. |
Masayo Yoshimori, Partha Lahiri, A second-order efficient empirical Bayes confidence interval, Annals of Statistics, 2014.08. |
| 12. |
Masayo Yoshimori, Partha Lahiri, A new adjusted maximum likelihood method for the Fay-Herriot small area model, Journal of Multivariate Analysis, 2014.02. |
| 13. |
Kazuko Uno, Katsumi Yagi, Masayo Yoshimori, Mari Tanigawa, Toshikazu Yoshikawa, Setsuya Fujita, IFN production ability and healthy ageing
Mixed model analysis of a 24 year longitudinal study in Japan, BMJ open, 10.1136/bmjopen-2012-002113, 3, 1, 2013.01. |
