A Unified Treatment of Predictive Model Comparison
arXiv:1506.02273
Abstract
The predictive performance of any inferential model is critical to its practical success, but quantifying predictive performance is a subtle statistical problem. In this paper I show how the natural structure of any inferential problem defines a canonical measure of relative predictive performance and then demonstrate how approximations of this measure yield many of the model comparison techniques popular in statistics and machine learning.
20 pages, 11 figures