[Statlist] seminar 29.11.2023

[MATEI Alina Gabriela]

The Institute of Statistics, University of Neuchâtel is organizing on Wednesday, November 29, 2023 at 11:00 the seminar "Measuring impact and reducing the dimension of auxiliary variables in the calibration estimator" by Alessio Guandalini (Italian National Institute of Statistics).

Room : GB31-33, Faculté des Sciences, Bâtiment G, Av. des Bellevaux 51, 2000, Neuchâtel

Abstract: In multipurpose surveys several interest variables and a very large number of auxiliary variables are collected. Auxiliary variables are usually considered in calibration (Deville and Särndal, 1992) for improving estimates. But, very often, some of them are included for the sole purpose of increasing consistency. Consistency is an important point for National Statistical Institutes especially as a means for promoting credibility in published statistics. As a direct result, the number of auxiliary variables considered in calibration continue to grow over time. In literature, several methods show how to manage many auxiliary variables in order to prevent some unpleasant consequences on the accuracy of estimates. They consist mainly in variable selection or dimension reduction and they are very useful for deriving calibrated estimates more accurately. Among these methods, there are forward and stepwise selection (Chambers et al., 1999 and Clark and Chambers, 2008), lasso (McConville et al., 2017), the partial least squares (Swold et al., 2001), the principal component analysis (PCA) (Cardot et al., 2017) and, recently, even machine learning techniques. However, looking at them, it is not easy to infer how much the contribution of each auxiliary variable is, especially when there are plenty of them. The Shapley decomposition (Shapley, 1953) applied in the calibration context could be a useful tool to better understand the net effects of auxiliary variables, and, in addition, it provides further information for supporting researchers in choosing the best calibration system. It provides a direct measure of the change with respect to Horvitz-Thompson (Horvitz and Thompson, 1952) estimates and to related sampling variances due to the introduction of each auxiliary variable in the calibration. An application on real data of the Italian Labour Force Survey (LFS) that makes an extensive use of auxiliary variables in calibration will be shown.


Main references
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Chambers, R. L., C. Skinner, S. Wang. 1999. Intelligent calibration. Bull. Int. Stat. Inst. 58(2): 321-324.
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Deville, J.-C., C.-E. Särndal. 1992. Calibration estimators in survey sampling. J. Am. Stat. Assoc. 87(418): 376-382.
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