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Robust estimation of mean squared prediction error in small‐area estimation
Author(s) -
Wu Ping,
Jiang Jiming
Publication year - 2021
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11567
Subject(s) - jackknife resampling , mean squared error , small area estimation , statistics , estimation , mean squared prediction error , mathematics , moment (physics) , regression , computer science , measure (data warehouse) , data mining , estimator , physics , management , classical mechanics , economics
The nested‐error regression model is one of the best‐known models in small area estimation. A small area mean is often expressed as a linear combination of fixed effects and realized values of random effects. In such analyses, prediction is made by borrowing strength from other related areas or sources and mean‐squared prediction error (MSPE) is often used as a measure of uncertainty. In this article, we propose a bias‐corrected analytical estimation of MSPE as well as a moment‐match jackknife method to estimate the MSPE without specific assumptions about the distributions of the data. Theoretical and empirical studies are carried out to investigate performance of the proposed methods with comparison to existing procedures.