New method improves accuracy of model selection in medical research.
The article explores how to make accurate predictions in statistics by using the Akaike information criterion. By analyzing different models, the researchers found that the selection process affects the accuracy of estimators. They discovered that the distribution of estimators depends on the set of competitive models and the smallest overparameterized model. Additionally, they found that even without a true model, they could still make reliable predictions using this method. The researchers used simulations and a diabetes dataset to confirm their findings.
