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SIS: An R package for sure independence screening in ultrahigh-dimensional statistical models
Diego Franco Saldana,
Yang Feng
Global Public Health
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peer-review
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Dive into the research topics of 'SIS: An R package for sure independence screening in ultrahigh-dimensional statistical models'. Together they form a unique fingerprint.
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Mathematics
Screening
80%
Statistical Model
70%
Independence
63%
Variable Selection
37%
Feature Selection
24%
Pseudo-likelihood
23%
Cox Proportional Hazards Model
23%
Thresholding
21%
Likelihood Methods
20%
Data-driven
20%
Generalized Linear Model
19%
Model Selection
17%
Penalty
16%
Regularization
15%
Covariates
15%
Performance
10%
Business & Economics
Statistical Model
100%
Screening
70%
Variable Selection
65%
Feature Selection
29%
Pseudo-likelihood
28%
Model Selection
27%
Cox Proportional Hazards Model
25%
Generalized Linear Model
23%
Regularization
23%
Recruiting
19%
Covariates
18%
Penalty
16%
Performance
6%
Engineering & Materials Science
Screening
67%
Statistical Models
65%
Hazards
16%
Feature extraction
13%