Skip to main navigation
Skip to search
Skip to main content
Princeton University Home
Help & FAQ
Home
Profiles
Research units
Facilities
Projects
Research output
Search by expertise, name or affiliation
A flexible framework for hypothesis testing in high dimensions
Adel Javanmard,
Jason D. Lee
Electrical and Computer Engineering
Center for Statistics & Machine Learning
Research output
:
Contribution to journal
›
Article
›
peer-review
10
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'A flexible framework for hypothesis testing in high dimensions'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Hypothesis Testing
97%
Higher Dimensions
89%
Framework
55%
Testing
54%
Confidence interval
48%
Covariates
32%
Minimax Rate
25%
High Power
23%
Type I Error Rate
22%
Type I error
21%
Convex Cone
21%
Conditioning
20%
Categorical or nominal
19%
Linear Functional
19%
Linear Regression Model
19%
Model
19%
Linear regression
18%
Exceed
17%
High-dimensional
15%
Duality
15%
Valid
14%
Numerical Experiment
13%
Unknown
12%
Demonstrate
11%
Arbitrary
10%
Business & Economics
Hypothesis Testing
100%
Confidence Interval
85%
Testing
64%
Type I Error
49%
Covariates
39%
Convex Cone
31%
Minimax
24%
Linear Regression Model
21%
Duality
20%
Linear Regression
19%
Numerical Experiment
19%
Conditioning
19%
Inference
16%