I performed a holdout cross-validation analysis on a multilevel model fit. The purpose of this was to show that we didn't have a problem with over-fitting, for which it worked just fine. Now we are writing it up for publication and I need a citation to support my methodology. I am looking for a good canonical statistical reference, ideally a book, that does a nice job explaining why holdout cross-validation is a real thing that people do and makes sense in this application. The paper will be published in a biological journal, so I am looking to point non-statistical types to a general reference. Somehow, none of my books seem to quite do it. The Wikipedia entry would be perfectly adequate for my purpose, but I'd rather not cite Wikipedia. Any suggestions?
I would suggest you to take a look at "Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. (2012). Learning from data (Vol. 4). New York, NY, USA:: AMLBook." This book has the "Chapter 4: overfitting" that explains why people use cross validation to prevent overfitting among other methods (e.g. regularization). The book even provides the theorem to support the claim. BTW, it's a nice book in other topics in machine learning.
Dear Hewa Ghafor Hassan,
You can take a look at the following:
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Article Preventing "Overfitting" of Cross-Validation Data
Article Study on the Impact of Partition-Induced Dataset Shift on -F...
Article What is an optimal value of k in k-fold cross-validation in ...
Hope this helps!
Regards,
Mohamed
Maybe this classic paper, with +12000 cites?
Article A Study of Cross-Validation and Bootstrap for Accuracy Estim...
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