Recently my colleague Ruth Keogh and I had a paper published: ‘Bayesian correction for covariate measurement error: a frequentist evaluation and comparison with regression calibration’ (open access here). The paper compares the popular regression calibration approach for handling covariate measurement error in regression models with a Bayesian approach. The two methods are compared from the frequentist perspective, and one of the arguments we make is that frequentists should more often consider using Bayesian methods.
Inference
On “The fallacy of placing confidence in confidence intervals”
Note: if you read this post, make sure to read the comments/discussion below it with Richard Morey, author of the paper in question, who put me straight on a number of points.
Thanks to Twitter I came across the latest draft of a very nicely written and thought provoking paper “The fallacy of placing confidence in confidence intervals”, by Morey, Rouder, Hoekstra, Lee and Wagenmakers. The paper aims to show why frequentist confidence intervals do not posses a number of properties that researchers often believe that they do. In contrast, they show that Bayesian credible intervals posses these desired properties, and advocate the replacement of confidence intervals with Bayesian credible intervals.
Banning p-values from journals
A psychology journal (Basic and Applied Social Psychology) has recently caused a bit of stir by banning p-values from their published articles. For what it’s worth, here’s a few views on the journal’s new policy, and on the use of p-values and confidence intervals in empirical research.