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Unbiased Survey Estimation with Population Auxiliary Variables

Abstract Number:

3571 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Robyn Ferg (1), Johann Gagnon-Bartsch (2), Jaylin Lowe (3), James Green (1)

Institutions:

(1) Westat, N/A, (2) N/A, N/A, (3) University of Michigan, N/A

Co-Author(s):

Johann Gagnon-Bartsch  
N/A
Jaylin Lowe  
University of Michigan
James Green  
Westat

First Author:

Robyn Ferg  
Westat

Presenting Author:

Robyn Ferg  
Westat

Abstract Text:

In many applications, population auxiliary variables and predictive models can be used to increase the precision and accuracy of survey estimates. We propose a new model-assisted approach that makes it possible incorporate model predictions into survey estimation to improve precision, while maintaining the unbiasedness property of the Horvitz-Thompson estimator. Our method allows for any prediction function or machine learning algorithm to be used to predict the response for out-of-sample observations. The unbiasedness property remains fully design-based and does not require the validity of the prediction model. We apply this estimation method to the Drug Abuse Warning Network, a national survey of hospital emergency department visits relating to substance abuse, showing an increase in accuracy for the national estimate of substance-related cases over traditional survey estimation methods.

Keywords:

model-assisted inference|survey estimation|auxiliary data|finite population inference|machine learning|regression

Sponsors:

Survey Research Methods Section

Tracks:

Data Analysis/Modeling

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