Premium
Copula‐based predictions in small area estimation
Author(s) -
Grover Kanika,
Acar Elif F.,
Torabi Mahmoud
Publication year - 2020
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.11558
Subject(s) - small area estimation , copula (linguistics) , estimator , best linear unbiased prediction , econometrics , statistics , parametric statistics , multivariate statistics , mean squared error , unbiased estimation , mathematics , computer science , artificial intelligence , selection (genetic algorithm)
Unit‐level regression models are commonly used in small area estimation (SAE) to obtain an empirical best linear unbiased prediction of small area characteristics. The underlying assumptions of these models, however, may be unrealistic in some applications. Previous work developed a copula‐based SAE model where the empirical Kendall's tau was used to estimate the dependence between two units from the same area. In this article, we propose a likelihood framework to estimate the intra‐class dependence of the multivariate exchangeable copula for the empirical best unbiased prediction (EBUP) of small area means. One appeal of the proposed approach lies in its accommodation of both parametric and semi‐parametric estimation approaches. Under each estimation method, we further propose a bootstrap approach to obtain a nearly unbiased estimator of the mean squared prediction error of the EBUP of small area means. The performance of the proposed methods is evaluated through simulation studies and also by a real data application.