ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1605.03321
  4. Cited By
Tuning parameter selection in high dimensional penalized likelihood

Tuning parameter selection in high dimensional penalized likelihood

11 May 2016
Yingying Fan
C. Tang
ArXivPDFHTML

Papers citing "Tuning parameter selection in high dimensional penalized likelihood"

10 / 10 papers shown
Title
Gibbs-Based Information Criteria and the Over-Parameterized Regime
Gibbs-Based Information Criteria and the Over-Parameterized Regime
Haobo Chen
Yuheng Bu
Greg Wornell
21
1
0
08 Jun 2023
Simultaneous Best Subset Selection and Dimension Reduction via
  Primal-Dual Iterations
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations
Canhong Wen
Ruipeng Dong
Xueqin Wang
Weiyu Li
Heping Zhang
14
0
0
29 Nov 2022
Communication-efficient Distributed Newton-like Optimization with
  Gradients and M-estimators
Communication-efficient Distributed Newton-like Optimization with Gradients and M-estimators
Ziyan Yin
25
0
0
13 Jul 2022
Forecasting: theory and practice
Forecasting: theory and practice
F. Petropoulos
D. Apiletti
Vassilios Assimakopoulos
M. Z. Babai
Devon K. Barrow
...
J. Arenas
Xiaoqian Wang
R. L. Winkler
Alisa Yusupova
F. Ziel
AI4TS
28
362
0
04 Dec 2020
A Survey of Tuning Parameter Selection for High-dimensional Regression
A Survey of Tuning Parameter Selection for High-dimensional Regression
Y. Wu
Lan Wang
29
35
0
10 Aug 2019
Tuning parameter selection rules for nuclear norm regularized
  multivariate linear regression
Tuning parameter selection rules for nuclear norm regularized multivariate linear regression
Pan Shang
Lingchen Kong
13
1
0
19 Jan 2019
Model selection with lasso-zero: adding straw to the haystack to better
  find needles
Model selection with lasso-zero: adding straw to the haystack to better find needles
Pascaline Descloux
S. Sardy
29
10
0
14 May 2018
Dependence Modeling in Ultra High Dimensions with Vine Copulas and the
  Graphical Lasso
Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso
D. Müller
C. Czado
16
15
0
15 Sep 2017
Forward-Backward Selection with Early Dropping
Forward-Backward Selection with Early Dropping
Giorgos Borboudakis
Ioannis Tsamardinos
16
95
0
30 May 2017
Model Selection Principles in Misspecified Models
Model Selection Principles in Misspecified Models
Jinchi Lv
Jun S. Liu
67
140
0
29 May 2010
1