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Penalized Weighted Least Squares to Small Area Estimation
Author(s) -
Zhu Rong,
Zou Guohua,
Liang Hua,
Zhu Lixing
Publication year - 2016
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12201
Subject(s) - mathematics , estimator , best linear unbiased prediction , mean squared error , statistics , robustness (evolution) , small area estimation , generalized least squares , mathematical optimization , computer science , artificial intelligence , selection (genetic algorithm) , biochemistry , chemistry , gene
In this paper, a penalized weighted least squares approach is proposed for small area estimation under the unit level model. The new method not only unifies the traditional empirical best linear unbiased prediction that does not take sampling design into account and the pseudo‐empirical best linear unbiased prediction that incorporates sampling weights but also has the desirable robustness property to model misspecification compared with existing methods. The empirical small area estimator is given, and the corresponding second‐order approximation to mean squared error estimator is derived. Numerical comparisons based on synthetic and real data sets show superior performance of the proposed method to currently available estimators in the literature.