Small area estimation methods under cut-off sampling

Maria Guadarrama, Isabel Molina Peralta, Yves Tillé

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Cut-off sampling is applied when there is a subset of units from the population from which getting the required information is too expensive or difficult and, therefore, those units are deliberately excluded from sample selection. If those excluded units are different from the sampled ones in the characteristics of interest, naïve estimators may be severely biased. Calibration estimators have been proposed to reduce the design-bias. However, when estimating in small domains, they can be inefficient even in the absence of cut-off sampling. Model-based small area estimation methods may prove useful for reducing the bias due to cut-off sampling if the assumed model holds for the whole population. At the same time, for small domains, these methods provide more efficient estimators than calibration methods. Since model-based properties are obtained assuming that the model holds but no model is exactly true, here we analyze the design properties of calibration and model-based procedures for estimation of small domain characteristics under cut-off sampling. Our results confirm that model-based estimators reduce the bias due to cut-off sampling and perform significantly better in terms of design mean squared error.
    Original languageEnglish
    Pages (from-to)51-75
    Number of pages27
    JournalSurvey Methodology
    Volume46
    Issue number1
    Publication statusPublished - 30 Jun 2020

    Keywords

    • Calibration estimators
    • Cut-off sampling
    • Empirical best linear unbiased predictor (EBLUP)
    • Empirical best/Bayes predictor (EBP)
    • Nested-error model
    • Unit level models

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