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On bias-robust mean squared error estimation for pseudo-linear small area estimators

Journal Article


Abstract


  • We propose a method of mean squared error (MSE) estimation for estimators of finite population domain means that can be

    expressed in pseudo-linear form, i.e., as weighted sums of sample values. In particular, it can be used for estimating the

    MSE of the empirical best linear unbiased predictor, the model-based direct estimator and the M-quantile predictor. The

    proposed method represents an extension of the ideas in Royall and Cumberland (1978) and leads to MSE estimators that

    are simpler to implement, and potentially more bias-robust, than those suggested in the small area literature. However, it

    should be noted that the MSE estimators defined using this method can also exhibit large variability when the area-specific

    sample sizes are very small. We illustrate the performance of the method through extensive model-based and design-based

    simulation, with the latter based on two realistic survey data sets containing small area information.

Authors


  •   Chambers, Raymond L.
  •   Chandra, Hukum (external author)
  •   Tzavidis, Nikos (external author)

Publication Date


  • 2011

Citation


  • Chambers, R. L., Chandra, H. & Tzavidis, N. (2011). On bias-robust mean squared error estimation for pseudo-linear small area estimators. Survey Methodology, 37 (2), 153-170.

Scopus Eid


  • 2-s2.0-84855282369

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3807

Number Of Pages


  • 17

Start Page


  • 153

End Page


  • 170

Volume


  • 37

Issue


  • 2

Place Of Publication


  • http://www.statcan.gc.ca/pub/12-001-x/2011002/article/11604-eng.pdf

Abstract


  • We propose a method of mean squared error (MSE) estimation for estimators of finite population domain means that can be

    expressed in pseudo-linear form, i.e., as weighted sums of sample values. In particular, it can be used for estimating the

    MSE of the empirical best linear unbiased predictor, the model-based direct estimator and the M-quantile predictor. The

    proposed method represents an extension of the ideas in Royall and Cumberland (1978) and leads to MSE estimators that

    are simpler to implement, and potentially more bias-robust, than those suggested in the small area literature. However, it

    should be noted that the MSE estimators defined using this method can also exhibit large variability when the area-specific

    sample sizes are very small. We illustrate the performance of the method through extensive model-based and design-based

    simulation, with the latter based on two realistic survey data sets containing small area information.

Authors


  •   Chambers, Raymond L.
  •   Chandra, Hukum (external author)
  •   Tzavidis, Nikos (external author)

Publication Date


  • 2011

Citation


  • Chambers, R. L., Chandra, H. & Tzavidis, N. (2011). On bias-robust mean squared error estimation for pseudo-linear small area estimators. Survey Methodology, 37 (2), 153-170.

Scopus Eid


  • 2-s2.0-84855282369

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3807

Number Of Pages


  • 17

Start Page


  • 153

End Page


  • 170

Volume


  • 37

Issue


  • 2

Place Of Publication


  • http://www.statcan.gc.ca/pub/12-001-x/2011002/article/11604-eng.pdf