ncpen-package: ncpen: A package for non-convex penalized estimation for...

In ncpen: Unified Algorithm for Non-convex Penalized Estimation for Generalized Linear Models

Description

This package fits the generalized linear models with various non-convex penalties. Supported regression models are Gaussian (linear), binomial Logit (logistic), multinomial Logit, Poisson and Cox proportional hazard. A unified algorithm is implemented in ncpen based on the convex concave procedure or difference convex algorithm that can be applied to most of existing non-convex penalties. The available penalties in the package are the least absolute shrinkage and selection operator(LASSO), smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated \ell_1-penalty (TLP), clipped LASSO (CLASSO), sparse bridge (SRIDGE), modified bridge (MBRIDGE), and modified log (MLOG) penalties.

Details

The package accepts a design matrix X and vector of responses y, and produces the regularization path over a grid of values for the tuning parameter lambda. Also provides user-friendly processes for plotting, selecting tuning parameters using cross-validation or generalized information criterion (GIC), \ell_2-regularization, penalty weights, standardization and intercept.

Note

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.

Author(s)

Dongshin Kim, Sunghoon Kwon and Sangin Lee

References

Kim, D., Lee, S. and Kwon, S. (2018). A unified algorithm for the non-convex penalized estimation: The ncpen package. http://arxiv.org/abs/1811.05061.

Kwon, S., Lee, S. and Kim, Y. (2016). Moderately clipped LASSO. Computational Statistics and Data Analysis, 92C, 53-67.

Lee, S., Kwon, S. and Kim, Y. (2016). A modified local quadratic approximation algorithm for penalized optimization problems. Computational Statistics and Data Analysis, 94, 275-286.

Choi, H., Kim, Y. and Kwon, S. (2013). Sparse bridge estimation with a diverging number of parameters. Statistics and Its Interface, 6, 231-242.

ncpen documentation built on May 1, 2019, 9:21 p.m.