Model-Robust Regression and a Bayesian `Sandwich' Estimator
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Abstract:
We present a new Bayesian approach to model-robust linear regression that leads to uncertainty estimates with the same robustness properties as the `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.
Subject Area:
Statistical Theory and Methods
Suggested Citation:
Adam A. Szpiro, Kenneth M. Rice, and Thomas Lumley, "Model-Robust Regression and a Bayesian `Sandwich' Estimator" (November 6, 2008). UW Biostatistics Working Paper Series. Working Paper 338.
http://www.bepress.com/uwbiostat/paper338