Abstract
PLEASE NOTE THAT AN UPDATED VERSION OF THIS RESEARCH IS AVAILABLE AS WORKING PAPER 338 IN THE UNIVERSITY OF WASHINGTON BIOSTATISTICS WORKING PAPER SERIES (http://www.bepress.com/uwbiostat/paper338).
In applied regression problems there is often sufficient data for accurate estimation, but standard parametric models do not accurately describe the source of the data, so associated uncertainty estimates are not reliable. We describe a simple Bayesian approach to inference in linear regression that recovers least-squares point estimates while providing correct uncertainty bounds by explicitly recognizing that standard modeling assumptions need not be valid. Our model-robust development parallels frequentist estimating equations and leads to intervals with the same robustness properties as the ’sandwich’ estimator.
Disciplines
Statistical Methodology | Statistical Theory
Suggested Citation
Szpiro, Adam A.; Rice, Kenneth M.; and Lumley, Thomas, "Model-Robust Bayesian Regression and the Sandwich Estimator" (December 2007). UW Biostatistics Working Paper Series. Working Paper 320.
https://biostats.bepress.com/uwbiostat/paper320