Predictive value of statistical models
- 1 November 1990
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 9 (11) , 1303-1325
- https://doi.org/10.1002/sim.4780091109
Abstract
A review is given of different ways of estimating the error rate of a prediction rule based on a statistical model. A distinction is drawn between apparent, optimum and actual error rates. Moreover it is shown how cross‐validation can be used to obtain an adjusted predictor with smaller error rate. A detailed discussion is given for ordinary least squares, logistic regression and Cox regression in survival analysis. Finally, the split‐sample approach is discussed and demonstrated on two data sets.Keywords
This publication has 34 references indexed in Scilit:
- How Biased is the Apparent Error Rate of a Prediction Rule?Journal of the American Statistical Association, 1986
- Cross-Validation, the Jackknife, and the Bootstrap: Excess Error Estimation in Forward Logistic RegressionJournal of the American Statistical Association, 1986
- Estimating the Error Rate of a Prediction Rule: Improvement on Cross-ValidationJournal of the American Statistical Association, 1983
- How Many Variables Should Be Entered in a Regression Equation?Journal of the American Statistical Association, 1983
- The Use of Balanced Half-Sample Replication in Cross-Validation StudiesJournal of the American Statistical Association, 1976
- The Predictive Sample Reuse Method with ApplicationsJournal of the American Statistical Association, 1975
- The Relationship Between Variable Selection and Data Agumentation and a Method for PredictionTechnometrics, 1974
- Mean Square Error of Prediction as a Criterion for Selecting VariablesTechnometrics, 1971
- Ridge Regression: Applications to Nonorthogonal ProblemsTechnometrics, 1970
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970