HCTR: Higher Criticism Tuned Regression
A novel searching scheme for tuning parameter in high-dimensional
penalized regression. We propose a new estimate of the regularization
parameter based on an estimated lower bound of the proportion of false
null hypotheses (Meinshausen and Rice (2006) <doi:10.1214/009053605000000741>).
The bound is estimated by applying the empirical null distribution of the higher
criticism statistic, a second-level significance testing, which is constructed
by dependent p-values from a multi-split regression and aggregation method
(Jeng, Zhang and Tzeng (2019) <doi:10.1080/01621459.2018.1518236>). An estimate
of tuning parameter in penalized regression is decided corresponding to the lower
bound of the proportion of false null hypotheses. Different penalized
regression methods are provided in the multi-split algorithm.
| Version: |
0.1.1 |
| Depends: |
R (≥ 3.4.0) |
| Imports: |
glmnet (≥ 2.0-18), harmonicmeanp (≥ 3.0), MASS, ncvreg (≥
3.11-1), Rdpack (≥ 0.11-0), stats |
| Published: |
2019-11-22 |
| Author: |
Tao Jiang [aut, cre] |
| Maintainer: |
Tao Jiang <tjiang8 at ncsu.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
HCTR results |
Documentation:
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