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December 2010 Model-robust regression and a Bayesian “sandwich” estimator
Adam A. Szpiro, Kenneth M. Rice, Thomas Lumley
Ann. Appl. Stat. 4(4): 2099-2113 (December 2010). DOI: 10.1214/10-AOAS362


We present a new Bayesian approach to model-robust linear regression that leads to uncertainty estimates with the same robustness properties as the Huber–White sandwich estimator. The sandwich estimator is known to provide asymptotically correct frequentist inference, even when standard modeling assumptions such as linearity and homoscedasticity in the data-generating mechanism are violated. Our derivation provides a compelling Bayesian justification for using this simple and popular tool, and it also clarifies what is being estimated when the data-generating mechanism is not linear. We demonstrate the applicability of our approach using a simulation study and health care cost data from an evaluation of the Washington State Basic Health Plan.


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Adam A. Szpiro. Kenneth M. Rice. Thomas Lumley. "Model-robust regression and a Bayesian “sandwich” estimator." Ann. Appl. Stat. 4 (4) 2099 - 2113, December 2010.


Published: December 2010
First available in Project Euclid: 4 January 2011

zbMATH: 1220.62025
MathSciNet: MR2829948
Digital Object Identifier: 10.1214/10-AOAS362

Keywords: Bayesian inference , estimating equations , Linear regression , robust regression , sandwich estimator

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.4 • No. 4 • December 2010
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