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UPS delivers optimal phase diagram in high-dimensional variable
  selection
v1v2 (latest)

UPS delivers optimal phase diagram in high-dimensional variable selection

25 October 2010
Pengsheng Ji
Jiashun Jin
ArXiv (abs)PDFHTML

Papers citing "UPS delivers optimal phase diagram in high-dimensional variable selection"

20 / 20 papers shown
Title
High-dimensional variable selection with heterogeneous signals: A
  precise asymptotic perspective
High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective
Saptarshi Roy
Ambuj Tewari
Ziwei Zhu
70
5
0
05 Jan 2022
Variable selection, monotone likelihood ratio and group sparsity
Variable selection, monotone likelihood ratio and group sparsity
C. Butucea
E. Mammen
M. Ndaoud
Alexandre B. Tsybakov
107
4
0
30 Dec 2021
Best subset selection is robust against design dependence
Best subset selection is robust against design dependence
Yongyi Guo
Ziwei Zhu
Jianqing Fan
53
7
0
03 Jul 2020
Weak Signal Inclusion Under Sparsity and Dependence
Weak Signal Inclusion Under Sparsity and Dependence
X. J. Jeng
Yifei Hu
34
1
0
28 Jun 2020
Iterative Algorithm for Discrete Structure Recovery
Iterative Algorithm for Discrete Structure Recovery
Chao Gao
A. Zhang
500
32
0
04 Nov 2019
On the rate of concentration of maxima in Gaussian arrays
On the rate of concentration of maxima in Gaussian arrays
Rafail Kartsioukas
Zhengyuan Gao
Stilian A. Stoev
50
1
0
09 Oct 2019
Fundamental Limits of Exact Support Recovery in High Dimensions
Fundamental Limits of Exact Support Recovery in High Dimensions
Zhengyuan Gao
Stilian A. Stoev
83
16
0
13 Nov 2018
Optimal variable selection and adaptive noisy Compressed Sensing
Optimal variable selection and adaptive noisy Compressed Sensing
M. Ndaoud
Alexandre B. Tsybakov
277
24
0
10 Sep 2018
Global testing under the sparse alternatives for single index models
Global testing under the sparse alternatives for single index models
Q. Lin
Zhigen Zhao
Jun S. Liu
32
0
0
04 May 2018
Variable Selection via Adaptive False Negative Control in Linear
  Regression
Variable Selection via Adaptive False Negative Control in Linear Regression
X. J. Jeng
Xiongzhi Chen
63
9
0
20 Apr 2018
Distribution-free Multiple Testing
Distribution-free Multiple Testing
E. Arias-Castro
Shiyun Chen
OOD
72
63
0
26 Apr 2016
Adaptive variable selection in nonparametric sparse additive models
Adaptive variable selection in nonparametric sparse additive models
C. Butucea
N. Stepanova
79
13
0
26 Aug 2015
Improving heritability estimation by a variable selection approach in
  sparse high dimensional linear mixed models
Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models
Anna Bonnet
Céline Lévy-Leduc
Elisabeth Gassiat
R. Toro
T. Bourgeron
64
5
0
22 Jul 2015
Higher Criticism for Large-Scale Inference, Especially for Rare and Weak
  Effects
Higher Criticism for Large-Scale Inference, Especially for Rare and Weak Effects
D. Donoho
Jiashun Jin
122
131
0
17 Oct 2014
Rare and Weak effects in Large-Scale Inference: methods and phase
  diagrams
Rare and Weak effects in Large-Scale Inference: methods and phase diagrams
Jiashun Jin
Tracy Ke
98
49
0
16 Oct 2014
Detection and Feature Selection in Sparse Mixture Models
Detection and Feature Selection in Sparse Mixture Models
Nicolas Verzélen
E. Arias-Castro
176
42
0
07 May 2014
Optimal classification in sparse Gaussian graphic model
Optimal classification in sparse Gaussian graphic model
Yingying Fan
Jiashun Jin
Zhigang Yao
106
38
0
21 Dec 2012
Variable Selection with Exponential Weights and $l_0$-Penalization
Variable Selection with Exponential Weights and l0l_0l0​-Penalization
E. Arias-Castro
Karim Lounici
157
8
0
13 Aug 2012
Optimality of Graphlet Screening in High Dimensional Variable Selection
Optimality of Graphlet Screening in High Dimensional Variable Selection
Jiashun Jin
Cun-Hui Zhang
Qi Zhang
125
45
0
29 Apr 2012
High-dimensional regression with unknown variance
High-dimensional regression with unknown variance
Christophe Giraud
S. Huet
Nicolas Verzélen
124
63
0
26 Sep 2011
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