Activity Number:
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340
- SPEED: Applications of Advanced Statistical Techniques in Complex Survey Data Analysis: Small Area Estimation, Propensity Scores, Multilevel Models, and More
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Survey Research Methods Section
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Abstract #328422
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Presentation
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Title:
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Bayesian Inference for Sample Surveys in the Presence of High-Dimensional Auxiliary Information
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Author(s):
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Yutao Liu* and Andrew Gelman and Qixuan Chen
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Companies:
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Columbia University and Columbia University and Columbia University
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Keywords:
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Sample Surveys;
High-Dimensional Auxiliary Information;
Bayesian Multilevel Modeling;
Stan
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Abstract:
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The National Drug Abuse Treatment System Survey (NDATSS) is a panel survey of substance abuse treatment programs in the United States. In 2013, the NDATSS conducted its first wave of a panel survey on residential non-opioid treatment programs (non-OTPs). A random sample of programs was selected from a sampling frame constructed using the 2010 National Survey of Substance Abuse Treatment Services (N-SSATS), the latest available annual census of all substance abuse treatment programs in the United States when the NDATSS was planned. From 2010 to 2013, the population of residential non-OTPs changed, with new programs opened and some old programs closed. To account for the change in the population as well as potential response bias, we propose a Bayesian multilevel model to improve the survey inference of population quantities in the NDATSS using the newly released 2013 N-SSATS data, which contains a rich profile of up-to-date information about all residential non-OTPs in the nation. We utilize machine learning methods to handle high-dimensional auxiliary variables in the 2013 N-SSATS data. Computation is implemented in the Bayesian inference engine Stan.
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Authors who are presenting talks have a * after their name.
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