Latent structureVariable Selection
MCP Penalized Regression
MCP (Minimax Concave Penalty) is a variable selection method developed by Zhang (2010) that uses a concave penalty function for automated feature selection. Like SCAD, MCP addresses bias in lasso by avoiding shrinkage of large coefficients, but uses a different penalty shape that is computationally simpler than SCAD.
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Sources
- Zhang, C. H. (2010). Nearly unbiased variable selection under minimax concave penalty. Annals of Statistics, 38(2), 894-942. DOI: 10.1214/09-AOS729 ↗
- Breheny, P., & Huang, J. (2011). Coordinate descent algorithms for nonconvex penalized regression. Annals of Applied Statistics, 5(1), 232-253. DOI: 10.1214/10-AOAS388 ↗
- Zhang, C. H., & Zhang, T. (2012). A general theory of concave regularized M-estimators. Statistical Science, 27(4), 506-537. DOI: 10.1214/12-STS399 ↗