Activity Number:
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172
- Thinking Outside the Box: Innovative Methods for Estimation and Inference for Surveys
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #320591
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Title:
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Inference in the Presence of Imputed Databased on Random Forests
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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 Franche-Comté and Université de Franche Comté
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Keywords:
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Nonparametric;
High-dimensional;
Variance estimation;
Reverse approach ;
Two-phase approach;
Machine learning
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Abstract:
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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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Authors who are presenting talks have a * after their name.