The areas of model selection and model evaluation for predictive modeling
have received extensive treatment in the statistics literature, leading to both
theoretical advances and practical methods based on covariance penalties and
other approaches. However, the majority of this work, and especially the
practical approaches, are based on the "Fixed-X assumption", where covariate
values are assumed to be non-random and known. By contrast, in most modern
predictive modeling applications, it is more reasonable to take the "Random-X"
view, where future prediction points are random and new. Read More