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Activity Number: 588 - GSS/SRMS/SSS Student Paper Award Winners
Type: Topic Contributed
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Survey Research Methods Section
Abstract #311062
Title: Robust Bayesian Non-Parametric Inference for Non-Probability Samples: An Attempt to Combine Sensor-Based Records with Traditional Survey Data
Author(s): Ali Rafei* and Michael R Elliott and Carol Flannagan
Companies: University of Michigan and University of Michigan and Transportation Research Institute, University of Michigan
Keywords: Big Data; doubly robust adjustment; pseudo-weighting; prediction modeling; Bayesian additive regression trees; naturalistic driving study
Abstract:

With the widespread availability of Big Data, concerns are raised over finite population inference for such large-scale non-probability samples. The existing adjustment methods rely heavily on the correct specification of the underlying models. In the presence of a relevant benchmark survey, one might consider a doubly robust estimator for the desired population quantity to protect against model misspecification. This method reconciles the idea of propensity weighting with that of prediction modeling in a way that estimates are consistent if either model holds. To further weaken the modeling assumptions, we propose a modified augmented inverse propensity weighting method that allows for more flexible non-parametric methods for prediction. In particular, we employ Bayesian additive regression trees which not only automatically capture non-linear associations, but also permit direct estimation of variance through the posterior predictive draws. Considering the National Household Travel Survey 2017 as a benchmark, we apply our method to the sensor-based naturalistic driving data from the second Strategic Highway Research Program.


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

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