A note on joint calibration estimators for totals and quantiles

TL;DR

This paper introduces a unified calibration method combining totals and quantiles, enhancing survey estimation accuracy and flexibility, especially in non-response and data integration scenarios.

Contribution

It extends existing calibration techniques by integrating quantile constraints into total calibration, providing a versatile, single-step solution for multiple estimation goals.

Findings

Improved estimation of means, totals, and quantiles in one step.

Enhanced handling of non-response and data integration issues.

Comparable or superior performance to existing methods in simulations.

Abstract

In this paper, we combine calibration for population totals proposed by Deville and S\"arndal (1992) with calibration for population quantiles introduced by Harms and Duchesne (2006). We also extend the pseudo-empirical likelihood method proposed by Chen, Sitter, and Wu (2002). This approach extends the calibration equations for totals by adding relevant constraints on quantiles of continuous variables observed in the data. The proposed approach can be easily applied to handle non-response and data integration problems, and results in a single vector of weights. Furthermore, it is a multipurpose solution, i.e. it makes it possible to improve estimates of means, totals and quantiles for a variable of interest in one step. In a limited simulation study, we compare the proposed joint approach with standard calibration, calibration using empirical likelihood and the correctly specified inverse probability weighting estimator. Open source software implementing the proposed method is available.