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High-dimensional estimation in a survey sampling framework, model-assisted and calibration points of view

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  • Camelia Goga

    (Université de Franche-Comté)

Abstract

In surveys, model-assisted estimators and calibration estimators, based on auxiliary information, are commonly used to obtain efficient estimators of population totals/means. Nowadays, it is no longer unusual to face high-dimensional auxiliary information. Incorporating too many auxiliary variables in model-assisted and calibration estimators may lead to a loss of efficiency. In this paper, I will discuss the asymptotic efficiency of model-assisted and calibration estimators based on high-dimensional auxiliary data and show that they may suffer from an additional variability in certain conditions. I will also present two techniques for improving the efficiency of model-assisted and calibration estimators in a high-dimensional framework: the first one is based on ridge-type penalization and the second one is based on dimension reduction through principal components.

Suggested Citation

  • Camelia Goga, 2025. "High-dimensional estimation in a survey sampling framework, model-assisted and calibration points of view," METRON, Springer;Sapienza Università di Roma, vol. 83(1), pages 5-29, April.
    • Handle: RePEc:spr:metron:v:83:y:2025:i:1:d:10.1007_s40300-024-00280-9
    DOI: 10.1007/s40300-024-00280-9
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