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Activity Number: 413 - Recent Advances in Statistical Modeling and Machine Learning for Official Statistics and Survey Methodology
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #322412
Title: Design Consistent Bayesian Tree Models
Author(s): Scott H. Holan and Diya Bhaduri* and Daniell Toth
Companies: University of Missouri/U.S. Census Bureau and University of Missouri and U.S. Bureau of Labor Statistics
Keywords: Bayesian; Informative sampling; Survey methodology; Tree; Unit-level models
Abstract:

Tree models provide a method for analyzing survey data because of the easy way they can handle a large number of variables with many interactions often found in this type of data. However, until recently design consistent tree modeling algorithms have not been available for use on data collected from a complex sample design. Design consistent algorithms are very desirable due to many potential applications of these methods to survey data. As these applications have become more complex, interest in modeling the conditional distribution at each node using more sophisticated models has grown. Bayesian tree modeling approaches with a prior distribution on the set of all possible tree models and then select the optimal model using a stochastic search have been developed for independent data, but there no methods for incorporating survey weights to produce design consistent models. Since the Bayesian framework allows for easily incorporating more complex models, we propose extending the Bayesian tree algorithm research to obtain a design consistent Bayesian tree model. The methods are illustrated through empirical simulation and an application to the Consumer Expenditure Survey.


Authors who are presenting talks have a * after their name.

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