no. 3
Discussion
Discussion of “Minimal penalties and the slope heuristics: a survey” by Sylvain Arlot
[Discussion sur « Pénalités minimales et heuristique de pente » par Sylvain Arlot]
Journal de la société française de statistique, Tome 160 (2019) no. 3, pp. 121-125.
commente Minimal penalties and the slope heuristics: a survey
[Pénalités minimales et heuristique de pente] 
@article{JSFS_2019__160_3_121_0,
     author = {Donoho, David and Gavish, Matan},
     title = {Discussion of {{\textquotedblleft}Minimal} penalties and the slope heuristics: a survey{\textquotedblright} by {Sylvain} {Arlot}},
     journal = {Journal de la soci\'et\'e fran\c{c}aise de statistique},
     pages = {121--125},
     publisher = {Soci\'et\'e fran\c{c}aise de statistique},
     volume = {160},
     number = {3},
     year = {2019},
     mrnumber = {4021415},
     zbl = {1431.62114},
     language = {en},
     url = {http://archive.numdam.org/item/JSFS_2019__160_3_121_0/}
}
TY  - JOUR
AU  - Donoho, David
AU  - Gavish, Matan
TI  - Discussion of “Minimal penalties and the slope heuristics: a survey” by Sylvain Arlot
JO  - Journal de la société française de statistique
PY  - 2019
SP  - 121
EP  - 125
VL  - 160
IS  - 3
PB  - Société française de statistique
UR  - http://archive.numdam.org/item/JSFS_2019__160_3_121_0/
LA  - en
ID  - JSFS_2019__160_3_121_0
ER  - 
%0 Journal Article
%A Donoho, David
%A Gavish, Matan
%T Discussion of “Minimal penalties and the slope heuristics: a survey” by Sylvain Arlot
%J Journal de la société française de statistique
%D 2019
%P 121-125
%V 160
%N 3
%I Société française de statistique
%U http://archive.numdam.org/item/JSFS_2019__160_3_121_0/
%G en
%F JSFS_2019__160_3_121_0
Donoho, David; Gavish, Matan. Discussion of “Minimal penalties and the slope heuristics: a survey” by Sylvain Arlot. Journal de la société française de statistique, Tome 160 (2019) no. 3, pp. 121-125. http://archive.numdam.org/item/JSFS_2019__160_3_121_0/

[Gavish and Donoho, 2014] Gavish, M., and D. L. Donoho. (2014). The optimal hard threshold for singular values is 4 / 3 . IEEE Transactions on Information Theory 60.8: 5040-5053. | MR

[Chatterjee, 2013] Chatterjee (2013). Matrix estimation by universal singular value thresholding. The Annals of Statistics 43.1: 177-214. | MR

[Bais and Silverstein, 2010] Bais, Z. and Silverstein, J. W.(2010). Bais, Z. and Silverstein, J. W. Spectral Analysis of Large Dimensional Random Matrices (2nd Edition). Springer New York | MR