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
DOI:	10.32614/CRAN.package.HCTR
Author:	Tao Jiang [aut, cre]
Maintainer:	Tao Jiang <tjiang8 at ncsu.edu>
License:	GPL-2
NeedsCompilation:	no
Materials:	README
CRAN checks:	HCTR results

Documentation:

Reference manual:

HCTR.html , HCTR.pdf

Downloads:

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 (arm64): HCTR_0.1.1.tgz, r-oldrel (arm64): HCTR_0.1.1.tgz, r-release (x86_64): HCTR_0.1.1.tgz, r-oldrel (x86_64): HCTR_0.1.1.tgz
Old sources:	HCTR archive

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