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RESEARCH PRODUCT

Imputation Procedures in Surveys Using Nonparametric and Machine Learning Methods: An Empirical Comparison

Mehdi DagdougDavid HazizaCamelia Goga

subject

FOS: Computer and information sciencesStatistics and ProbabilityStatistics::ApplicationsEmpirical comparisonbusiness.industryComputer scienceApplied MathematicsNonparametric statisticsMachine learningcomputer.software_genreStatistics - ComputationVariety (cybernetics)Methodology (stat.ME)Set (abstract data type)Statistics::MethodologyImputation (statistics)Artificial intelligenceStatistics Probability and UncertaintybusinesscomputerStatistics - MethodologyComputation (stat.CO)Social Sciences (miscellaneous)

description

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 used next for the estimation of study parameters defined as solution of population estimating equation. 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.

yearjournalcountryeditionlanguage
2020-07-13Journal of Survey Statistics and Methodology
https://doi.org/10.1093/jssam/smab004