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This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals.
Approximate tests of correlation in time-series
Maurice H Quenouille · 1949
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On dominance relations and the structure of animal societies: Iii the condition for a score structure
HG Landau · 1953
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Notes on bias in estimation
Maurice H Quenouille · 1956
Earlier work this paper cites.
Bias and confidence in not quite large samples
John Tukey · 1958
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The jackknife–a review
Rupert G Miller · 1974
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Cross-validatory choice and assessment of statistical predictions
Mervyn Stone · 1974
Earlier work this paper cites.
The predictive sample reuse method with applications
Seymour Geisser · 1975
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Distribution-free inequalities for the deleted and holdout error estimates
Luc Devroye and Terry Wagner · 1979
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Bootstrap methods: Another look at the jackknife
Bradley Efron · 1979
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Predictive intervals based on reuse of the sample
Ronald Butler and Edward D Rothman · 1980
Earlier work this paper cites.
A leisurely look at the bootstrap, the jackknife, and cross-validation
Bradley Efron and Gail Gong · 1983
Earlier work this paper cites.
Bootstrap prediction intervals for regression
Robert A Stine · 1985
Cited alongside, same era.
Stability and generalization
Olivier Bousquet and André Elisseeff · 2002
Cited alongside, same era.
A data-driven software tool for enabling cooperative information sharing among police departments
Michael Redmond and Alok Baveja · 2002
Cited alongside, same era.
Algorithmic Learning in a Random World
Vladimir Vovk, Alex Gammerman, and Glenn Shafer · 2005
Cited alongside, same era.
Sample design of the medical expenditure panel survey household component, 1998-2007
Trena M Ezzati-Rice, Frederick Rohde, and Janet Greenblatt · 2008
Cited alongside, same era.
Inductive conformal prediction: Theory and application to neural networks
Harris Papadopoulos · 2008
Cited alongside, same era.
Efficiency of conformalized ridge regression
Evgeny Burnaev and Vladimir Vovk · 2014
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Feedback prediction for blogs
Krisztian Buza · 2014
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Cross-conformal predictors
Vladimir Vovk · 2015
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Leave-one-out prediction intervals in linear regression models with many variables
Lukas Steinberger and Hannes Leeb · 2016
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Fast exact conformalization of lasso using piecewise linear homotopy
Jing Lei · 2017
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Discretized conformal prediction for efficient distribution-free inference
Wenyu Chen, Kelli-Jean Chun, and Rina Foygel Barber · 2018
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
Conditional validity of inductive conformal predictors
Vladimir Vovk · 2012
Cited alongside, same era.
Combining p-values via averaging
Vladimir Vovk and Ruodu Wang · 2012
Cited alongside, same era.
Sparse algorithms are not stable: A no-free-lunch theorem
Huan Xu, Constantine Caramanis, and Shie Mannor · 2012
Cited alongside, same era.
Later among the works it cites.
Distribution-free predictive inference for regression
Jing Lei, Max G’Sell, Alessandro Rinaldo, Ryan J Tibshirani, and Larry Wasserman · 2018
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Conditional predictive inference for high-dimensional stable algorithms
Lukas Steinberger and Hannes Leeb · 2018
Later among the works it cites.
Cross-conformal predictive distributions
Vladimir Vovk, Ilia Nouretdinov, Valery Manokhin, and Alexander Gammerman · 2018
Later among the works it cites.
Surprises in high-dimensional ridgeless least squares interpolation
Trevor Hastie, Andrea Montanari, Saharon Rosset, and Ryan J Tibshirani · 2019
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