Prediction Error and Its Estimation for Subset-Selected Models
- 1 November 1991
- journal article
- research article
- Published by JSTOR in Technometrics
- Vol. 33 (4), 459-468
- https://doi.org/10.2307/1269417
Abstract
Strategies are compared for development of a linear regression model and the subsequent assessment of its predictive ability. Simulations were performed as a designed experiment over a range of data structures. Approaches using a forward selection of variables resulted in slightly smaller prediction errors and less biased estimators of predictive accuracy than all possible subsets selection but often did not improve on the full model. Random and balanced data splitting resulted in increased prediction errors and estimators with large mean squared error. To maximize predictive accuracy while retaining a reliable estimate of that accuracy, it is recommended that the entire sample usually he used for model development and assessment.Keywords
This publication has 1 reference indexed in Scilit:
- Algorithm AS 111: The Percentage Points of the Normal DistributionJournal of the Royal Statistical Society Series C: Applied Statistics, 1977