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) ). 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) ). 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.


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0.1.1 by Tao Jiang, a year ago

Browse source code at https://github.com/cran/HCTR

Authors: Tao Jiang [aut, cre]

Documentation:   PDF Manual  

GPL-2 license

Imports glmnet, harmonicmeanp, MASS, ncvreg, Rdpack, stats

See at CRAN