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

Camelia Goga ()
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Camelia Goga: Université de Franche-Comté

METRON, 2025, vol. 83, issue 1, No 2, 5-29

Abstract: 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.

Keywords: High-dimensional data sets; Over-calibration; Principal component regression; Ridge regression (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40300-024-00280-9

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