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Estimators of error covariance matrices for small area prediction

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  • Berg, Emily J.
  • Fuller, Wayne A.

Abstract

Prediction for the mixed model requires estimates of covariance matrices. There is often a direct estimate of the “within area” covariance matrix, and for survey samples this is an estimate of the sampling covariance matrix. The estimated covariance matrix may have large sampling variance, suggesting parametric modeling for the matrix. The model can play a role at various points in the construction of predictions for proportions for small areas. Simulations demonstrate that efficiency for predictions is improved by using a model for the covariance matrix in the estimator of mean parameters and in constructing the coefficients in the predictor.

Suggested Citation

  • Berg, Emily J. & Fuller, Wayne A., 2012. "Estimators of error covariance matrices for small area prediction," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2949-2962.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:10:p:2949-2962
    DOI: 10.1016/j.csda.2012.02.030
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    References listed on IDEAS

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    1. Li‐Chun Zhang & Raymond L. Chambers, 2004. "Small area estimates for cross‐classifications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 479-496, May.
    2. Wang, Junyuan & Fuller, Wayne A., 2003. "The Mean Squared Error of Small Area Predictors Constructed With Estimated Area Variances," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 716-723, January.
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