The role of models in model-assisted and model-dependent estimation for domains and small areas

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    Estimation for population subgroups or domains is investigated for model-assisted generalized regression (GREG) and model-dependent EBLUP estimators, under different model choices and under unequal probability sampling. Two particular issues are addressed: (i) how to account for the domain differences in the model formulation, and (ii) how to account for the underlying unequal probability sampling design. Results on bias and accuracy of GREG and EBLUP are based on Monte Carlo experiments where PPS samples were drawn from an artificially generated population. The bias of GREG estimator remained negligible for all model formulations considered, and accuracy improved when including the PPS size variable in the assisting model. A “double-use” of the auxiliary data both
    in the sampling design and in the estimation design appeared favorable. In GREG, the mixed model formulation did not outperform the fixed-effects model formulation. For EBLUP, the model choice was critical and if not successful, large bias was introduced. For unweighted EBLUP, substantial bias reduction was attained with the inclusion of the PPS size variable in the model. We propose a new weighted EBLUP estimator for unequal probability sampling designs, as an alternative to the unweighted EBLUP. The results show that the weighted EBLUP behaves better that the unweighted EBLUP, but still the bias can be substantial and can dominate the MSE, which invalidates the construction of proper confidence intervals.
    Original languageEnglish
    Title of host publicationProceedings of the Workshop on Survey Sampling Theory and Methodology, August 24-28, 2006, Ventspils, Latvia
    Number of pages10
    Publication date2006
    Publication statusPublished - 2006
    MoE publication typeA4 Article in conference proceedings
    EventWorkshop on Survey Sampling Theory and Methodology - Riga, Poland
    Duration: 1 Jan 1800 → …

    Bibliographical note

    Central Statistical Bureau of Latvia;

    Proceeding volume:

    Fields of Science

    • 112 Statistics and probability

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