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

arXiv: https://arxiv.org/abs/2007.06298 · HTML: https://mlsanity.com/paper/2007.06298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study](https://mlsanity.com/paper/2412.13570/) (2024)

## Abstract (arXiv)

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.