Skip to main navigation
Skip to search
Skip to main content
Experts@Minnesota Home
Home
Profiles
Research units
University Assets
Projects and Grants
Research output
Press/Media
Datasets
Activities
Fellowships, Honors, and Prizes
Search by expertise, name or affiliation
Regularized rank-based estimation of high-dimensional nonparanormal graphical models
Lingzhou Xue,
Hui Zou
Statistics (Twin Cities)
Research output
:
Contribution to journal
›
Article
›
peer-review
163
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Regularized rank-based estimation of high-dimensional nonparanormal graphical models'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Graphical Models
85%
High-dimensional
59%
Oracle
25%
Selector
19%
Lasso
18%
Joint Distribution
15%
Gaussian distribution
13%
Graphics
13%
Data Transformation
11%
Interpretability
10%
Model
10%
Unknown
10%
Variable Transformation
10%
Estimator
9%
Gaussian Model
9%
Model Selection
7%
Normality
7%
Monotone
6%
Sample Size
6%
Demonstrate
4%
Performance
4%
Zero
4%
Business & Economics
Graphical Models
100%
Matrix
21%
Normal Distribution
17%
Estimator
12%
Model Selection
12%
Sample Size
10%
Normality
9%
Performance
3%