A permutation approach to validation*

DOI10.1002/sam.10096OpenAlexW1996371471MaRDI QIDQ4969738

Konstantin Mertsalov, Malik Magdon-Ismail

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.10096

zbMATH Keywords

model selectionuniform boundsout-sample error

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (1)

Guarantees for the Kronecker fast Johnson-Lindenstrauss transform using a coherence and sampling argument

Uses Software

• UCI-ml
• PRMLT

Cites Work

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• Some limit theorems for empirical processes (with discussion)
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Adaptive model selection using empirical complexities
• Permutation, parametric and bootstrap tests of hypotheses.
• Model selection by bootstrap penalization for classification
• Statistical predictor identification
• A New Formula for Predicting the Shrinkage of the Coefficient of Multiple Correlation
• Rademacher penalties and structural risk minimization
• Structural risk minimization over data-dependent hierarchies
• 10.1162/153244303321897690
• Learning Theory
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• The Estimation of Prediction Error
• Some applications of concentration inequalities to statistics
• Model selection and error estimation
• A new look at the statistical model identification

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