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Imputation Procedures in Surveys Using Nonparametric and Machine Learning Methods: An Empirical Comparison.
- Source :
-
Journal of Survey Statistics & Methodology . Feb2023, Vol. 11 Issue 1, p141-188. 48p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MACHINE learning
*EMPIRICAL research
*STATISTICAL learning
Subjects
Details
- Language :
- English
- ISSN :
- 23250984
- Volume :
- 11
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Survey Statistics & Methodology
- Publication Type :
- Academic Journal
- Accession number :
- 161603037
- Full Text :
- https://doi.org/10.1093/jssam/smab004