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Cross-validation is one of the most popular model selection methods in statistics and machine learning.
On the increase of dispersion of sums of independent random variables
Boris Alekseevich Rogozin · 1961
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The relationship between variable selection and data agumentation and a method for prediction
David M Allen · 1974
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Cross-validatory choice and assessment of statistical predictions
Mervyn Stone · 1974
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The predictive sample reuse method with applications
Seymour Geisser · 1975
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Asymptotic optimality for cp, cl, cross-validation and generalized cross-validation: discrete index set
Ker-Chau Li · 1987
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Linear model selection by cross-validation
Jun Shao · 1993
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Model selection via multifold cross validation
Ping Zhang · 1993
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Weak Convergence and Empirical Processes
Aad W van der Vaart and Jon A Wellner · 1996
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Least angle regression
Bradley Efron, Trevor Hastie, Iain Johnstone, and Robert Tibshirani · 2004
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Consistency of cross validation for comparing regression procedures
Yuhong Yang · 2007
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Self-normalized processes: Limit theory and Statistical Applications
Victor H de la Peña, Tze Leung Lai, and Qi-Man Shao · 2008
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Fence methods for mixed model selection
Jiming Jiang, J Sunil Rao, Zhonghua Gu, and Thuan Nguyen · 2008
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High-dimensional generalized linear models and the lasso
Sara A Van de Geer · 2008
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Simultaneous analysis of lasso and dantzig selector
Peter J Bickel, Ya’acov Ritov, and Alexandre B Tsybakov · 2009
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Lasso-type recovery of sparse representations for high-dimensional data
Nicolai Meinshausen and Bin Yu · 2009
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Bi-cross-validation of the svd and the nonnegative matrix factorization
Art B Owen and Patrick O Perry · 2009
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A bias correction for the minimum error rate in cross-validation
Ryan J Tibshirani and Robert Tibshirani · 2009
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Introduction to nonparametric estimation, 2009
Alexandre B Tsybakov · 2009
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Consistent cross-validation for tuning parameter selection in high-dimensional variable selection
Yang Feng and Yi Yu · 2013
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Risk-consistency of cross-validation with lasso-type procedures
Darren Homrighausen and Daniel J McDonald · 2013
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The bernstein–orlicz norm and deviation inequalities
Sara van de Geer and Johannes Lederer · 2013
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Prediction error of cross-validated lasso
Sourav Chatterjee and Jafar Jafarov · 2015
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Comparison and anti-concentration bounds for maxima of gaussian random vectors
Victor Chernozhukov, Denis Chetverikov, and Kengo Kato · 2015
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High dimensional variable selection
Larry Wasserman and Kathryn Roeder · 2009
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The model confidence set
Peter R Hansen, Asger Lunde, and James M Nason · 2011
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Consistent tuning parameter selection in high dimensional sparse linear regression
Tao Wang and Lixing Zhu · 2011
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Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
Victor Chernozhukov, Denis Chetverikov, and Kengo Kato · 2013
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Testing many moment inequalities
Victor Chernozhukov, Denis Chetverikov, and Kengo Kato · 2013
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Tuning parameter selection in high dimensional penalized likelihood
Yingying Fan and Cheng Yong Tang · 2013
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Confidence sets for model selection by f-testing
Davide Ferrari and Yuhong Yang · 2015
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Tuning parameter selection for the adaptive lasso using eric
Francis KC Hui, David I Warton, and Scott D Foster · 2015
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Network cross-validation for determining the number of communities in network data
Kehui Chen and Jing Lei · 2016
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Denis Chetverikov and Zhipeng Liao · 2016
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Estimation stability with cross-validation (escv)
Chinghway Lim and Bin Yu · 2016
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On the prediction performance of the lasso
Arnak S Dalalyan, Mohamed Hebiri, Johannes Lederer, et al · 2017
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Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2017
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