So I have developed some logistic models, and obviously the next step is to validate them. I have no option for external validation, so internal is the way to go for me. On that note, I have been looking into this article (https://www.ncbi.nlm.nih.gov/pubmed/11470385) by Steyerberg and Harrell, which pretty much talks about internal validation.
So if I am understanding the paper correctly, the most accurate validation is achieved with regular bootstrapping - not even the .632 or +0.632 methods. In general I like bootstrapping more than CV, although often more computer intensive, but I just wanted to make sure that this was the way to go, or maybe that’s different from case to case ?
For example, what I usually hear is that bootstrapping estimates variability where CV is meant to quantify predictive accuracy. But in this paper it seems all things can be achieved through bootstrapping ?
Am I missing something here, or…?