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arXiv · 2007.06298

Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison

Mehdi Dagdoug·Camelia Goga·David Haziza
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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.

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Mehdi Dagdoug, Camelia Goga, David Haziza. 2020-07-13. Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison. https://doi.org/10.1093/jssam/smab004

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