Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
0905.2171
Cited By

Dimension reduction and variable selection in case control studies via
regularized likelihood optimization

v1v2 (latest)

Dimension reduction and variable selection in case control studies via regularized likelihood optimization

13 May 2009

ArXiv (abs)PDF HTML

Papers citing "Dimension reduction and variable selection in case control studies via regularized likelihood optimization"

5 / 5 papers shown

Calibrated Multivariate Regression with Application to Neural Semantic
Basis Discovery

Calibrated Multivariate Regression with Application to Neural Semantic Basis DiscoveryJournal of machine learning research (JMLR), 2013

443

36

0

10 May 2013

The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms

The Group Square-Root Lasso: Theoretical Properties and Fast AlgorithmsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2013

Johannes Lederer

462

112

0

01 Feb 2013

Tight conditions for consistency of variable selection in the context of
high dimensionality

Tight conditions for consistency of variable selection in the context of high dimensionality

774

66

0

21 Jun 2011

Tight conditions for consistent variable selection in high dimensional
nonparametric regression

Tight conditions for consistent variable selection in high dimensional nonparametric regressionAnnual Conference Computational Learning Theory (COLT), 2011

443

15

0

17 Feb 2011

SPADES and mixture models

SPADES and mixture models

Alexandre B. Tsybakov

447

65

0

14 Jan 2009

Page 1 of 1