Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Asymptotic statistical theory of overtraining and cross-validation
IEEE Transactions on Neural Networks
| Hi-index | 7.29 |
The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regression model parameters as a multivariate variable, propose to estimate the sample size using the distance between parameter distribution functions on cross-validated data sets. Herewith, the authors give a new contribution to data mining and statistical learning, supported by applied mathematics.