Penalized Weighted Least Squares to Small Area Estimation

Rong Zhu*, Guohua Zou, Hua Liang, Lixing ZHU

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)736-756
Number of pages21
JournalScandinavian Journal of Statistics
Issue number3
Publication statusPublished - 2016

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • basic unit level model
  • inverse probability weighted estimation
  • penalized least squares
  • robustness
  • small area estimation


Dive into the research topics of 'Penalized Weighted Least Squares to Small Area Estimation'. Together they form a unique fingerprint.

Cite this