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- W3041151490 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." @default.
- W3041151490 created "2020-07-16" @default.
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- W3041151490 date "2020-07-13" @default.
- W3041151490 modified "2023-09-27" @default.
- W3041151490 title "Imputation procedures in surveys using nonparametric and machine learning methods: an empirical comparison" @default.
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