Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1151646 | Statistical Methodology | 2015 | 15 Pages |
Abstract
Nonconvex penalties (such as the smoothly clipped absolute deviation penalty and the minimax concave penalty) have some attractive properties including the unbiasedness, continuity and sparsity, and the ridge regression can deal with the collinearity problem. Combining the strengths of nonconvex penalties and ridge regression (abbreviated as NPR), we study the oracle selection property of the NPR estimator for high-dimensional partially linear additive models with highly correlated predictors, where the dimensionality of covariates pnpn is allowed to increase exponentially with the sample size nn. Simulation studies and a real data analysis are carried out to show the performance of the NPR method.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Mingqiu Wang,