ABSTRACT
Having discussed at length the deleterious impact of treating mismeasured explanatory variables as if they are precisely measured, we now turn to methods of analysis which recognize and adjust for such mismeasurement. This chapter considers adjustments for the mismeasurement of continuous explanatory variables. Whereas the discussion in Chapters 2 and 3 about the performance of naive estimation in the presence of mismeasurement applies to different methods of statistical inference, we now turn to Bayesian inference implemented with MCMC algorithms as a route to adjust for mismeasurement. Readers with little background in the Bayes-MCMC approach are advised to consult the Appendix, and perhaps some of the references cited therein, before delving into what follows.
