ncpen
Unified Algorithm for Non-convex Penalized Estimation for Generalized Linear Models
An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. doi:10.48550/arXiv.1811.05061, doi:10.1016/j.csda.2015.08.019, doi:10.1016/j.csda.2015.07.001. (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.)
- Version1.0.0
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?Yes
- Languageen-US
- doi:10.48550/arXiv.1811.05061
- doi:10.1016/j.csda.2015.08.019
- doi:10.1016/j.csda.2015.07.001
- Last release11/17/2018
Documentation
Team
Dongshin Kim
Sunghoon Kwon
Show author detailsRolesAuthor, Copyright holderSangin Lee
Show author detailsRolesAuthor, Copyright holder
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