Sparse Generalized Linear Models
Sparse Generalized Linear Models is a class of generalized linear models that incorporates regularization or structural assumptions to achieve parsimonious parameter estimates by driving a subset of coefficients to exactly zero, thereby enabling variable selection and model interpretability in high-dimensional settings.
84
Publications
6.4K
Citations
191
Authors
118
Institutions
Publications per year
2017–2026
27
Parents
84
Degrees of freedom in lasso problems
Ryan J. Tibshirani, Jonathan Taylor · Figshare · 2012 · 353 citations · Full text
Degrees of freedom in lasso problems
The Annals of Statistics · 2012 · 236 citations · Full text
Extended BIC for small-n-large-P sparse GLM
Jiahua Chen, Zehua Chen · Statistica Sinica · 2011 · 201 citations
Computing, Biostatistics, Genetics +16
1–5 of 84
191
Massachusetts Institute of Technology, United States
3 publications · 206 citations
3
h-index
3
h-index
3
h-index
2
h-index
2
h-index
1–5 of 191
118
University of Michigan
Ann Arbor, United States
6 h-index
6 publications
291 citations
Stanford University
Stanford, United States
5 h-index
12 publications
1.2K citations
Texas A&M University
College Station, United States
4 h-index
8 publications
389 citations
North Carolina State University
Raleigh, United States
4 h-index
5 publications
255 citations

University of Electro-Communications
Tokyo, Japan
3 h-index
3 publications
61 citations
RIKEN Center for Advanced Intelligence Project
Tokyo, Japan
3 h-index
3 publications
41 citations
Columbia University
New York, United States
3 h-index
3 publications
227 citations
University of Southern California
Los Angeles, United States
3 h-index
4 publications
209 citations
1–8 of 118
Repository · 13 publications · 405 citations
12
h-index
6 publications · 382 citations
6
h-index
CSDA · 4 publications · 101 citations
4
h-index
4 publications · 1.8K citations
4
h-index
3 publications · 116 citations
3
h-index
1–5