29 May 2024

IASS Webinar 40: An Estimation of Variance of Random Effects to Solve Multiple Problems in Small Area Estimation

Date 29 May 2024
Time 12:00 GMT+02:00 - 13:30 GMT+02:00
Level of instruction Intermediate
Masayo Hirose
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Speaker:  Masayo Y. Hirose
Institute of Mathematics for Industry, Kyushu University, Japan 

Webinar Abstract
For several decades, area-level models have played a critical role in the theory and practice of small area estimation. For an area-level model, we propose a random effects variance estimator that simultaneously (i) improves on the estimation of the related shrinkage factors, (ii) protects empirical best linear unbiased predictors (EBLUP) of the random effects from the common over-shrinkage problem, (iii) avoids complex bias correction in generating strictly positive second-order unbiased mean square error (MSE) estimator either by the Taylor series or single parametric bootstrap method. The idea of achieving multiple desirable properties in an EBLUP method through a suitably devised random effects variance estimator is the first of its kind and holds promise in providing good inferences for random effects under the EBLUP framework. The proposed methodology is also evaluated using a Monte Carlo simulation study and real data analysis. This is a joint work with Prof. Partha Lahiri at the University of Maryland, College Park. 



Masayo Hirose
Masayo Hirose

About the instructor

Masayo Hirose received her Ph.D. degree in Engineering from Osaka University, Japan, in 2014. She is now an assistant professor at the Institute of Mathematics for Industry of Kyushu University, Japan. Her research interests include a model-based Approach to Small Area Estimation, linear mixed Models, and Empirical Bayes estimation. She has been a visiting research scholar at the University of Maryland, College Park, since Oct. 2024.