A class of Bivariate SURE estimators in heteroscedastic hierarchical normal models
- 10.2991/jsta.2018.17.2.11How to use a DOI?
- Asymptotic univariate shrinkage estimators; Heteroscedasticity; Hierarchical models; Multivariate shrinkage estimator; Stein’s unbiased risk estimators
In this paper, we first propose a class of bivariate shrinkage estimators based on Steins unbiased estimate of risk (SURE). Then, we study the effect of correlation coefficients on their performance. Moreover, under some mild assumptions on the model correlations, we set up the optimal asymptotic properties of our estimates when the number of vector means to be estimated grows. Furthermore, we carry out a simulation study to compare how various bivariate competing shrinkage estimators perform and analyze a real data set.
- Copyright © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).
Cite this article
TY - JOUR AU - S.K. Ghoreishi PY - 2018 DA - 2018/06/30 TI - A class of Bivariate SURE estimators in heteroscedastic hierarchical normal models JO - Journal of Statistical Theory and Applications SP - 324 EP - 339 VL - 17 IS - 2 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.2018.17.2.11 DO - 10.2991/jsta.2018.17.2.11 ID - Ghoreishi2018 ER -