Error estimation based on variance analysis of k -fold cross-validation

This publication has 19 references indexed in Scilit:

• Blocked 3×2 Cross-Validated t-Test for Comparing Supervised Classification Learning Algorithms
  Neural Computation, 2014
• A general framework for the statistical analysis of the sources of variance for classification error estimators
  Pattern Recognition, 2013
• Study on the Impact of Partition-Induced Dataset Shift on $k$-Fold Cross-Validation
  IEEE Transactions on Neural Networks and Learning Systems, 2012
• On the use of cross-validation for time series predictor evaluation
  Information Sciences, 2012
• Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression
  Journal of the Royal Statistical Society Series B: Statistical Methodology, 2011
• On the sampling distribution of resubstitution and leave-one-out error estimators for linear classifiers
  Pattern Recognition, 2009
• Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap
  Computational Statistics & Data Analysis, 2009
• Cross-validation and bootstrapping are unreliable in small sample classification
  Pattern Recognition Letters, 2008
• Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression
  Pattern Recognition, 2007
• Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms
  Neural Computation, 1999