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 |
| Reference manual: | HCTR.pdf |
| Package source: | HCTR_0.1.1.tar.gz |
| Windows binaries: | r-devel: HCTR_0.1.1.zip, r-release: HCTR_0.1.1.zip, r-oldrel: HCTR_0.1.1.zip |
| macOS binaries: | r-release: HCTR_0.1.1.tgz, r-oldrel: HCTR_0.1.1.tgz |
| Old sources: | HCTR archive |
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