Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison
Methodology
2022-08-23 v2 Computation
Abstract
Nonparametric and machine learning methods are flexible methods for obtaining accurate predictions. Nowadays, data sets with a large number of predictors and complex structures are fairly common. In the presence of item nonresponse, nonparametric and machine learning procedures may thus provide a useful alternative to traditional imputation procedures for deriving a set of imputed values. In this paper, we conduct an extensive empirical investigation that compares a number of imputation procedures in terms of bias and efficiency in a wide variety of settings, including high-dimensional data sets. The results suggest that a number of machine learning procedures perform very well in terms of bias and efficiency.
Cite
@article{arxiv.2007.06298,
title = {Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison},
author = {Mehdi Dagdoug and Camelia Goga and David Haziza},
journal= {arXiv preprint arXiv:2007.06298},
year = {2022}
}