Cross-validation failure: Small sample sizes lead to large error bars

Predictive models ground many state-of-the-art developments in statistical brain image analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach to establish their validity and usefulness is cross-validation, testing prediction on unseen data. Here, I would like to...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 180; H. Pt A; S. 68 - 77
1. Verfasser:	Varoquaux, Gaël
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 15.10.2018 Elsevier Limited Elsevier
Schlagworte:	Accuracy Bioinformatics Biomarkers Brain Mapping - methods Brain Mapping - standards Cognitive ability Cognitive science Computer Science Cross-validation Decoding Experiments fMRI Humans Image processing Image Processing, Computer-Assisted - methods Image Processing, Computer-Assisted - standards Machine Learning Magnetic Resonance Imaging - methods Magnetic Resonance Imaging - standards Medical imaging Methodology Model selection MVPA Neuroimaging Neuroscience Psychology Reproducibility of Results Sample Size Simulation Statistical analysis Statistics fMRI Model selection Cross-validation Biomarkers Decoding Statistics MVPA biomarkers model selection Comments and Controversies cross-validation decoding statistics
ISSN:	1053-8119, 1095-9572, 1095-9572
Online-Zugang:	Volltext
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