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Activity Number: 154 - Some New Innovations in Survey Sampling and Missing Data Problems
Type: Topic-Contributed
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: Survey Research Methods Section
Abstract #317142
Title: Random Forests Imputation in Surveys
Author(s): Mehdi Dagdoug* and Camelia Goga and David Haziza
Companies: Université de Bourgogne Franche Comté and Université de Bourgogne Franche Comté and University of Ottawa
Keywords: survey; missing data; random forest; imputation

Item nonresponse in surveys is usually handled through some form of imputation. Random forests provide flexible tools for obtaining a set of imputed values. We lay out a set of sufficient conditions needed for establishing the $L^2$-consistency of an imputed estimator based on random forests. We consider several random forests algorithms and establish the $L^2$-consistency for each algorithm. We derive the asymptotic variance of imputed estimators and propose a consistent variance estimator. We present the results from a simulation study that investigates the performance of point and variance estimators based on random forest imputation in terms of bias, efficiency and coverage rate.

Authors who are presenting talks have a * after their name.

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