Discrepancy-Based Model Selection Criteria Using Cross-Validation

A model selection criterion is often formulated by constructing an approximately unbiased estimator of an expected discrepancy, a measure that gauges the separation between the true model and a fitted approximating model. The expected discrepancy reflects how well, on average, the fitted approximating model predicts “new” data generated under the true model. A related measure, the estimated discrepancy, reflects how well the fitted approximating model predicts the data at hand.