On optimal data split for generalization estimation and model selection

The paper is concerned with studying the very different behavior of the two data splits using hold-out cross-validation, K-fold cross-validation and randomized permutation cross-validation. First we describe the theoretical basics of various cross-validation techniques with the purpose of reliably estimating the generalization error and optimizing the model structure. The paper deals with the simple problem of estimating a single location parameter. This problem is tractable as non-asymptotic theoretical analysis is possible, whereas mainly asymptotic analysis and simulation studies are viable for the more complex AR-models and neural networks.