Variable Selection
Variable Selection is the statistical process of identifying a subset of predictors from a larger set of potential variables to include in a model based on their relevance to the outcome, balancing model fit and parsimony within the context of Bayesian inference, predictive accuracy, risk factor identification, and inferential validity.
3K
Publications
365.7K
Citations
6.7K
Authors
2.3K
Institutions
Publications per year
2017–2026
956
3K
The Adaptive Lasso and Its Oracle Properties
Hui Zou · Journal of the American Statistical Association · 2006 · 7.6K citations
1–5 of 3K
6.7K
18
h-index
16
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16
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15
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12
h-index
1–5 of 6.7K
2.3K
Texas A&M University
College Station, United States
28 h-index
56 publications
4.6K citations
University of Wisconsin–Madison
Madison, United States
27 h-index
56 publications
14.3K citations
North Carolina State University
Raleigh, United States
26 h-index
75 publications
5.2K citations
University of Pennsylvania
Philadelphia, United States
24 h-index
66 publications
7.8K citations
University of Michigan
Ann Arbor, United States
23 h-index
70 publications
6.5K citations
University of Chicago
Chicago, United States
22 h-index
49 publications
13.2K citations

Central South University
Changsha, China
21 h-index
102 publications
9.8K citations
National University of Singapore
Singapore, Singapore
21 h-index
56 publications
11.6K citations
1–8 of 2.3K
115 publications · 37.1K citations
63
h-index
74 publications · 7.8K citations
46
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69 publications · 6.9K citations
36
h-index
CSDA · 81 publications · 4.9K citations
35
h-index
48 publications · 9.8K citations
35
h-index
1–5