Fetching the paper…
Reading the bibliography…
Standard high-dimensional regression methods assume that the underlying coefficient vector is sparse.
An introduction to multivariate statistical analysis , volume 2
Theodore Wilbur Anderson · 1958
Earlier work this paper cites.
Distribution of eigenvalues for some sets of random matrices
Vladimir Alexandrovich Marchenko and Leonid Andreevich Pastur · 1967
Earlier work this paper cites.
Arbitrage, factor structure, and mean-variance analysis on large asset markets
Gary Chamberlain and Michael Rothschild · 1982
Earlier work this paper cites.
Confounding and collapsibility in causal inference
Sander Greenland, James M Robins, and Judea Pearl · 1999
Earlier work this paper cites.
Inferential theory for factor models of large dimensions
Jushan Bai · 2003
Earlier work this paper cites.
Confidence intervals for diffusion index forecasts and inference for factor-augmented regressions
Jushan Bai and Serena Ng · 2006
Earlier work this paper cites.
Capturing heterogeneity in gene expression studies by surrogate variable analysis
Jeffrey T Leek and John D Storey · 2007
Earlier work this paper cites.
Interpreting principal component analyses of spatial population genetic variation
John Novembre and Matthew Stephens · 2008
Earlier work this paper cites.
Genes mirror geography within europe
John Novembre, Toby Johnson, Katarzyna Bryc, Zoltán Kutalik, Adam R Boyko, Adam Auton, Amit Indap, Karen S King, Sven Bergmann, Matthew R Nelson, et al · 2008
Earlier work this paper cites.
”preconditioning” for feature selection and regression in high-dimensional problems
Debashis Paul, Eric Bair, Trevor Hastie, and Robert Tibshirani · 2008
Earlier work this paper cites.
Simultaneous analysis of lasso and dantzig selector
Peter J Bickel, Ya’acov Ritov, Alexandre B Tsybakov, et al · 2009
Earlier work this paper cites.
Confounding control in healthcare database research: challenges and potential approaches
M Alan Brookhart, Til Stürmer, Robert J Glynn, Jeremy Rassen, and Sebastian Schneeweiss · 2010
Earlier work this paper cites.
Introduction to the non-asymptotic analysis of random matrices
Roman Vershynin · 2010
Cited alongside, same era.
Statistics for high-dimensional data: methods, theory and applications
Peter Bühlmann and Sara van de Geer · 2011
Cited alongside, same era.
Variable selection through correlation sifting
Jim C Huang and Nebojsa Jojic · 2011
Cited alongside, same era.
Robust statistics
Peter J Huber · 2011
Cited alongside, same era.
Latent variable graphical model selection via convex optimization
Venkat Chandrasekaran, Pablo A Parrilo, and Alan S Willsky · 2012
Cited alongside, same era.
Using control genes to correct for unwanted variation in microarray data
Johann A Gagnon-Bartsch and Terence P Speed · 2012
Cited alongside, same era.
Estimation and testing under sparsity
Sara Van de Geer · 2016
Later among the works it cites.
High dimensional probability
Roman Vershynin · 2016
Later among the works it cites.
Adaptive estimation in structured factor models with applications to overlapping clustering
Xin Bing, Florentina Bunea, Yang Ning, and Marten Wegkamp · 2017
Later among the works it cites.
A lava attack on the recovery of sums of dense and sparse signals
Victor Chernozhukov, Christian Hansen, Yuan Liao, et al · 2017
Later among the works it cites.
Empirical bayes shrinkage and false discovery rate estimation, allowing for unwanted variation
David Gerard and Matthew Stephens · 2017
Later among the works it cites.
Detecting non-causal artifacts in multivariate linear regression models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Optimal shrinkage of eigenvalues in the spiked covariance model
David L Donoho, Matan Gavish, and Iain M Johnstone · 2013
Cited alongside, same era.
Large covariance estimation by thresholding principal orthogonal complements
Jianqing Fan, Yuan Liao, and Martina Mincheva · 2013
Cited alongside, same era.
Introduction to high-dimensional statistics
Christophe Giraud · 2014
Cited alongside, same era.
Statistical learning with sparsity: the lasso and generalizations
Trevor Hastie, Robert Tibshirani, and Martin Wainwright · 2015
Cited alongside, same era.
Preconditioning the lasso for sign consistency
Jinzhu Jia, Karl Rohe, et al · 2015
Cited alongside, same era.
Dominik Janzing and Bernhard Schölkopf · 2018
Closest in time.
Rsvp-graphs: Fast high-dimensional covariance matrix estimation under latent confounding
Rajen Shah and Nicolai Meinshausen · 2018
Closest in time.
The blessings of multiple causes
Yixin Wang and David M Blei · 2018
Closest in time.
Xin Bing, Florentina Bunea, Marten Wegkamp, and Seth Strimas-Mackey · 2019
Closest in time.
High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
Closest in time.
Factor-adjusted regularized model selection
Jianqing Fan, Yuan Ke, and Kaizheng Wang · 2020
Closest in time.