Estimating prediction error in microarray classification: Modifications of the 0.632+ bootstrap when ${\bf n} < {\bf p}$

DOI10.1002/CJS.11158zbMath1273.62098OpenAlexW2126130225WikidataQ58386640 ScholiaQ58386640MaRDI QIDQ2852557

Bingshu E. Chen, Wenyu Jiang

Publication date: 9 October 2013

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11158

zbMATH Keywords

cross-validationlearning curvefeature selectionclass prediction

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric statistical resampling methods (62G09) Genetics and epigenetics (92D10)

Uses Software

• ElemStatLearn

Cites Work

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• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• Improvements on Cross-Validation: The .632+ Bootstrap Method
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• Selection bias in gene extraction on the basis of microarray gene-expression data

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