A resampling approach to estimation of the linking variance in the Fay–Herriot model  

作　　者：Snigdhansu Chatterjee 

机构地区：[1]School of Statistics,University of Minnesota,Minneapolis,MN,USA

出　　处：《Statistical Theory and Related Fields》2019年第2期170-177,共8页统计理论及其应用（英文）

基　　金：This research is partially supported by the National Science Foundation(NSF)[grant numbers#DMS-1622483 and#DMS-1737918].

摘　　要：In the Fay–Herriot model,we consider estimators of the linking variance obtained using different types of resampling schemes.The usefulness of this approach is that even when the estimator from the original data falls below zero or any other specified threshold,several of the resamples can potentially yield values above the threshold.We establish asymptotic consistency of the resampling-based estimator of the linking variance for a wide variety of resampling schemes and show the efficacy of using the proposed approach in numeric examples.

关 键 词：Linking variance Prasad–Rao estimator paired bootstrap m-out-of-n bootstrap Bayesian bootstrap 

分 类 号：O24[理学—计算数学]