Statistical Theory for High-Dimensional Models

Regression analysis and selection via the Lasso

R. Tibshirani · 1996

Earlier work this paper cites.

Atomic decomposition by basis pursuit

S. S. Chen, D. L. Donoho, and M. A. Saunders · 1998

Earlier work this paper cites.

The Concentration of Measure Phenomenon , volume 89

M. Ledoux · 2005

Earlier work this paper cites.

The Generic Chaining

M. Talagrand · 2005

Earlier work this paper cites.

The deterministic Lasso

S.A. van de Geer · 2007

Earlier work this paper cites.

Sparse inverse covariance estimation with the graphical Lasso

J. Friedman, T. Hastie, and R. Tibshirani · 2008

Cited alongside, same era.

Square-root Lasso: pivotal recovery of sparse signals via conic programming

A. Belloni, V. Chernozhukov, and L. Wang · 2011

Cited alongside, same era.

Statistics for High-Dimensional Data: Methods, Theory and Applications

P. Bühlmann and S. van de Geer · 2011

Cited alongside, same era.

Nuclear-norm penalization and optimal rates for noisy low-rank matrix completion

V. Koltchinskii, K. Lounici, and A.B. Tsybakov · 2011

Cited alongside, same era.

High-dimensional covariance estimation by minimizing ℓ 1 \ell_{1} -penalized log-determinant divergence

P. Ravikumar, M.J. Wainwright, G. Raskutti, and B. Yu · 2011

Cited alongside, same era.

Scaled sparse linear regression

T. Sun and C.-H. Zhang · 2012

Later among the works it cites.

Concentration Inequalities: A Nonasymptotic Theory of Independence

S. Boucheron, G. Lugosi, and P. Massart · 2013

Later among the works it cites.

Confidence intervals for high-dimensional inverse covariance estimation, 2014

Original

J. Jankova and S. van de Geer · 2014

Closest in time.

On asymptotically optimal confidence regions and tests for high-dimensional models

S. van de Geer, P. Bühlmann, Y. Ritov, and R. Dezeure · 2014

Closest in time.

Confidence intervals for low dimensional parameters in high dimensional linear models

Original

C.-H. Zhang and S. S. Zhang · 2014

Closest in time.