A novel searching scheme for tuning parameter in highdimensional 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 secondlevel significance testing, which is constructed by dependent pvalues from a multisplit 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 multisplit algorithm.
Package details 


Author  Tao Jiang [aut, cre] 
Maintainer  Tao Jiang <[email protected]> 
License  GPL2 
Version  0.1.0 
Package repository  View on CRAN 
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