Robust Bayesian Inference for Big Data: Combining Sensor-based Records with Traditional Survey Data

TL;DR

This paper introduces a robust Bayesian method that combines sensor data with survey data to address bias in large non-probability samples, utilizing Bayesian additive regression trees for flexible modeling and uncertainty quantification.

Contribution

It develops a double robust Bayesian approach using non-parametric models to improve bias correction in big data with unknown selection mechanisms.

Findings

Effective bias adjustment demonstrated on driving data

Bayesian additive regression trees capture non-linear effects

Uncertainty quantification through posterior predictive draws

Abstract

Big Data often presents as massive non-probability samples. Not only is the selection mechanism often unknown, but larger data volume amplifies the relative contribution of selection bias to total error. Existing bias adjustment approaches assume that the conditional mean structures have been correctly specified for the selection indicator or key substantive measures. In the presence of a reference probability sample, these methods rely on a pseudo-likelihood method to account for the sampling weights of the reference sample, which is parametric in nature. Under a Bayesian framework, handling the sampling weights is an even bigger hurdle. To further protect against model misspecification, we expand the idea of double robustness such that more flexible non-parametric methods, as well as Bayesian models, can be used for prediction. In particular, we employ Bayesian additive regression trees, which not only capture non-linear associations automatically but permit direct quantification of the uncertainty of point estimates through its posterior predictive draws. We apply our method to sensor-based naturalistic driving data from the second Strategic Highway Research Program using the 2017 National Household Travel Survey as a benchmark.