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Principal regression for high dimensional covariance matrices
Yi Zhao,
Brian Caffo
, Xi Luo
Bloomberg School of Public Health
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Article
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peer-review
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Dive into the research topics of 'Principal regression for high dimensional covariance matrices'. Together they form a unique fingerprint.
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Mathematics
Covariance matrix
81%
High-dimensional
73%
Regression
69%
Alzheimer's Disease
63%
Functional Magnetic Resonance Imaging
61%
Quadratic Loss
29%
Shrinkage Estimator
28%
Sample Covariance Matrix
28%
Linear Estimator
27%
Estimator
26%
Unit matrix
26%
Shrinkage
25%
Brain
24%
Generalized Linear Model
23%
Conditioning
23%
Linear regression
22%
Regularity Conditions
21%
Connectivity
20%
Likelihood
19%
Simulation Study
16%
Unit
15%
Model
14%
Formulation
14%
Demonstrate
13%
Performance
13%
Coefficient
11%
Business & Economics
Covariance Matrix
100%
Alzheimer's Disease
58%
Functional Magnetic Resonance Imaging
46%
Matrix
31%
Estimator
28%
Shrinkage Estimator
27%
Generalized Linear Model
23%
Shrinkage
23%
Connectivity
20%
Linear Regression
18%
Conditioning
18%
Regularity
18%
Simulation Study
17%
Coefficients
13%
Performance
6%