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• W2041601239 abstract "The penalized calibration technique in survey sampling combines usual calibration and soft calibration by introducing a penalty term. Certain relevant estimates in survey sampling can be considered as penalized calibration estimates obtained as particular cases from an optimization problem with a common basic structure. In this framework, a case deletion diagnostic is proposed for a class of penalized calibration estimators including both design-based and model-based estimators. The diagnostic compares finite population parameter estimates and can be calculated from quantities related to the full data set. The resulting diagnostic is a function of the residual and leverage, as other diagnostics in regression models, and of the calibration weight, a singular feature in survey sampling. Moreover, a particular case, which includes the basic unit level model for small area estimation, is considered. Both a real and an artificial example are included to illustrate the diagnostic proposed. The results obtained clearly show that the proposed diagnostic depends on the calibration and soft-calibration variables, on the penalization term, as well as on the parameter to estimate." @default.
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• W2041601239 date "2015-07-01" @default.
• W2041601239 modified "2023-10-04" @default.
• W2041601239 title "A case-deletion diagnostic for penalized calibration estimators and BLUP under linear mixed models in survey sampling" @default.
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• W2041601239 doi "https://doi.org/10.1016/j.csda.2015.01.004" @default.
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