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Abstract Details
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Activity Number:
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671
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #304907 |
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Title:
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Endogenous Post-Stratification Estimation Using a Random Forests Model
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Author(s):
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John Tipton*+ and Jean Opsomer and Gretchen Moisen
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Companies:
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Colorado State University and Colorado State University and U.S. Forest Service
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Address:
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2212 Vassar Ave, Fort Collins, CO, 80525, United States
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Keywords:
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Endogenous Post Stratification ;
Random Forests ;
Variance Estimation
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Abstract:
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Post-stratification is used in survey statistics as a method to improve variance estimates. In traditional post-stratification methods, the variable on which the data is being stratified on must be known at the population level. In many cases this is not possible, but it is possible to use a model to predict values using covariates, and then stratify on these predicted values. This method is called Endogenous Post-Stratification Estimation (EPSE), originally proposed in Breidt and Opsomer (2008). In this presentation, we investigate methods to automatically select the number of post-strata for EPSE. We do this in the context of models fitted by Random Forests (Breiman 2001) with the stratum boundaries set at quantiles of the predicted distribution.
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