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Activity Number: 172 - Thinking Outside the Box: Innovative Methods for Estimation and Inference for Surveys
Type: Invited
Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #320591
Title: Inference in the Presence of Imputed Databased on Random Forests
Author(s): David Haziza* and Mehdi Dagdoug and Camelia Goga
Companies: University of Ottawa and Université de Franche-Comté and Université de Franche Comté
Keywords: Nonparametric; High-dimensional; Variance estimation; Reverse approach ; Two-phase approach; Machine learning
Abstract:

Item nonresponse in surveys is usually handled through some form of single imputation. Random forests provide flexible tools for obtaining a set of imputed values. Belonging to the class of non-parametric methods, random forests have the ability to capture nonlinear trends in the data and tend to be robust to the non-inclusion of interactions or predictors accounting for curvature. We lay out a set of sufficient conditions needed for establishing the L2-consistency of an imputed estimator based on random forests. We investigate the performance of variance estimators that account for sampling and nonresponse. We present the results from a simulation study to assess the proposed methods in terms of bias, efficiency and coverage rate of normal-based confidence intervals.


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