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Activity Number:
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420
- Modern Modeling Approaches for Imputation Using Survey Data
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Type:
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Invited
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Date/Time:
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Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #316718
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Title:
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Imputation Procedures in Surveys Using Nonparametric and Machine Learning Methods: An Empirical Comparison
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Author(s):
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David Haziza* and Mehdi Dagdoug and Camelia Goga
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Companies:
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University of Ottawa and Université de Bourgogne Franche Comté and Université de Bourgogne Franche Comté
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Keywords:
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Additive models;
Boosting;
Random forests;
Neareast-neighbour imputation;
Cubist;
BART
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Abstract:
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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 presentation, we will present the results of an extensive empirical investigation that compares a number of imputation procedures in terms of bias and efficiency in a wide variety of settings.
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Authors who are presenting talks have a * after their name.
