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Several new methods have been proposed for performing valid inference after model selection.
Optimal-order bounds on the rate of convergence to normality in the multivariate delta method
Iosif Pinelis and Raymond Molzon · 1935
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Dividing a sample into two parts a statistical dilemma
PAP Moran · 1973
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Discussion of “cross-validatory choice and assessment of statistical predictions” by stone
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A note on data-splitting for the evaluation of significance levels
DR Cox · 1975
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Harold Sackrowitz and Ester Samuel-Cahn · 1986
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A central limit theorem applicable to robust regression estimators
Stephen Portnoy · 1987
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Honest confidence regions for nonparametric regression
Ker-Chau Li · 1989
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The impact of model selection on inference in linear regression
Clifford M Hurvich and Chih—Ling Tsai · 1990
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Subset selection in regression
Alan Miller · 1990
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Data splitting
Richard R Picard and Kenneth N Berk · 1990
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Linear model selection by cross-validation
Jun Shao · 1993
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Data splitting strategies for reducing the effect of model selection on inference
Julian J Faraway · 1995
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Efficient and Adaptive Estimation for Semiparametric Models
Peter J. Bickel, Chris A. J. Klaassen, Ya’acov Ritov, and Wellner Jon A · 1998
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Frequentist model average estimators
Nils Lid Hjort and Gerda Claeskens · 2003
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On the maximal perimeter of a convex set in ℝ n \mathbb{R}^{n} with respect to a gaussian measure
Fedor Nazarov · 2003
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Normal approximation for nonlinear statistics using a concentration inequality approach
Louis HY Chen and Qi-Man Shao · 2007
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Matrix Differential Calculus with Applications in Statistics and Econometrics
Jan R. Magnus and Heinz Neudecker · 2007
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Can one estimate the unconditional distribution of post-model-selection estimators?
Hannes Leeb and Benedikt M Pötscher · 2008
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Hybrid and size-corrected subsampling methods
Donald WK Andrews and Patrik Guggenberger · 2009
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p-values for high-dimensional regression
Nicolai Meinshausen, Lukas Meier, and Peter Buhlmann · 2009
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Introduction to nonparametric estimation
Alexandre B Tsybakov · 2009
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High-dimensional variable selection
Larry Wasserman and Kathryn Roeder · 2009
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Stability selection
Nicolai Meinshausen and Peter Bühlmann · 2010
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Inference for high-dimensional sparse econometric models
Alexandre Belloni, Victor Chernozhukov, and Christian Hansen · 2011
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Bootstrapping lasso estimators
A. Chatterjee and S. N. Lahiri · 2011
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Matrix Analysis
Roger A. Horn and Charles R. Johnson · 2012
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A tail inequality for quadratic forms of subgaussian random vectors
Daniel Hsu, Sham Kakade, and Tong Zhang · 2012
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User-friendly tail bounds for sums of random matrices
Joel A. Tropp · 2012
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Valid post-selection inference
Richard Berk, Lawrence Brown, Andreas Buja, Kai Zhang, Linda Zhao, et al · 2013
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Controlling the false discovery rate via knockoffs
Rina Foygel Barber, Emmanuel J Candès, et al · 2015
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Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems
Alexandre Belloni, Victor Chernozhukov, and Kengo Kato · 2015
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High-dimensional inference in misspecified linear models
Peter Bühlmann and Sara van de Geer · 2015
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Models as approximations–a conspiracy of random regressors and model deviations against classical inference in regression
Andreas Buja, Richard Berk, Lawrence Brown, Edward George, Emil Pitkin, Mikhail Traskin, Linda Zhao, and Kai Zhang · 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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Statistical significance in high-dimensional linear models
Peter Buhlmann · 2013
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Rates of convergence of the adaptive lasso estimators to the oracle distribution and higher order refinements by the bootstrap
A. Chatterjee and S. N. Lahiri · 2013
Cited alongside, same era.
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
Victor Chernozhukov, Denis Chetverikov, Kengo Kato, et al · 2013
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Confidence sets in sparse regression
Richard Nickl and Sara van de Geer · 2013
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Variable selection with error control: another look at stability selection
Rajen D Shah and Richard J Samworth · 2013
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Bounds for the normal approximation of the maximum likelihood estimator
Andreas Anastasiou and Gesine Reinert · 2014
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High-dimensional inference: Confidence intervals, p p -values and r-software hdi
Ruben Dezeure, Peter Bühlmann, Lukas Meier, Nicolai Meinshausen, et al · 2015
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Can we trust the bootstrap in high-dimension?
Noureddine El Karoui and Elizabeth Purdom · 2015
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Selective inference in regression models with groups of variables
Joshua R Loftus and Jonathan E Taylor · 2015
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Group bound: confidence intervals for groups of variables in sparse high dimensional regression without assumptions on the design
Nicolai Meinshausen · 2015
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Bootstrap consistency for quadratic forms of sample averages with increasing dimension
Demian Pouzo et al · 2015
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Uniform asymptotic inference and the bootstrap after model selection
Ryan J Tibshirani, Alessandro Rinaldo, Robert Tibshirani, and Larry Wasserman · 2015
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Selective inference with a randomized response
Jonathan Taylor Xiaoying Tian · 2015
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Multivariate normal approximation of the maximum likelihood estimator via the delta method
Andreas Anastasiou and Robert E Gaunt · 2016
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Uniformly valid confidence intervals post-model-selection
François Bachoc, David Preinerstorfer, and Lukas Steinberger · 2016
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Panning for gold: Model-free knockoffs for high-dimensional controlled variable selection
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High-dimensional simultaneous inference with the bootstrap
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Distribution-free predictive inference for regression
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Bootstrap inference after using multiple queries for model selection
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Stein’s method for nonlinear statistics: A brief survey and recent progress
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Exact post-selection inference for sequential regression procedures
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Comparison of prediction errors: Adaptive p-values after cross-validation
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Simultaneous inference for high-dimensional linear models
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Goodness-of-fit tests for high dimensional linear models
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