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In a recent paper, Nguyen, Kuhn, and Esfahani (2018) built a distributionally robust estimator for the precision matrix of the Gaussian distribution.
Introduction to Mathematical Statistics
Robert V Hogg and Allen T Craig · 1978
Earlier work this paper cites.
An Introduction to Multivariate Statistical Analysis (3rd ed.)
T.W. Anderson · 2003
Earlier work this paper cites.
Topics in Optimal Transportation
Cedric Villani · 2003
Earlier work this paper cites.
Theory of Point Estimation
Erich L Lehmann and George Casella · 2006
Earlier work this paper cites.
Large Deviations Techniques and Applications, Second Edition
Amir Dembo and Ofer Zeitouni · 2009
Cited alongside, same era.
Probability: Theory and Examples
Rick Durrett · 2010
Cited alongside, same era.
Matrix Computations
Gene H Golub and Charles F Van Loan · 2012
Cited alongside, same era.
Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional gaussian distributions
T Tony Cai, Tengyuan Liang, and Harrison H Zhou · 2015
Cited alongside, same era.
Limit laws of the empirical Wasserstein distance: Gaussian distributions
Thomas Rippl, Axel Munk, and Anja Sturm · 2016
Later among the works it cites.
Distributionally robust inverse covariance estimation: The Wasserstein shrinkage estimator
Viet Anh Nguyen, Daniel Kuhn, and Peyman Mohajerin Esfahani · 2018
Later among the works it cites.
Supplementary material to the paper ”Optimal Uncertainty Size in Distributionally Robust Inverse Covariance Estimation”
Jose Blanchet and Nian Si · 2019
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