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BIOINFORMATICS
2005
109views more  BIOINFORMATICS 2005»
13 years 4 months ago
Prediction error estimation: a comparison of resampling methods
In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future obser...
Annette M. Molinaro, Richard Simon, Ruth M. Pfeiff...
CSDA
2010
119views more  CSDA 2010»
13 years 4 months ago
Fast robust estimation of prediction error based on resampling
Robust estimators of the prediction error of a linear model are proposed. The estimators are based on the resampling techniques cross-validation and bootstrap. The robustness of t...
Jafar A. Khan, Stefan Van Aelst, Ruben H. Zamar
WSC
2001
13 years 6 months ago
Resampling methods for input modeling
Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities b...
Russell R. Barton, Lee Schruben
BMCBI
2005
163views more  BMCBI 2005»
13 years 4 months ago
Rank-invariant resampling based estimation of false discovery rate for analysis of small sample microarray data
Background: The evaluation of statistical significance has become a critical process in identifying differentially expressed genes in microarray studies. Classical p-value adjustm...
Nitin Jain, HyungJun Cho, Michael O'Connell, Jae K...
IJCAI
1989
13 years 6 months ago
An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods
Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previous...
Sholom M. Weiss, Ioannis Kapouleas