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  "Title": "Higher Criticism Tuned Regression",
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  "Description": "A novel searching scheme for tuning parameter in\nhigh-dimensional penalized regression. We propose a new\nestimate of the regularization parameter based on an estimated\nlower bound of the proportion of false null hypotheses\n(Meinshausen and Rice (2006) <doi:10.1214/009053605000000741>).\nThe bound is estimated by applying the empirical null\ndistribution of the higher criticism statistic, a second-level\nsignificance testing, which is constructed by dependent\np-values from a multi-split regression and aggregation method\n(Jeng, Zhang and Tzeng (2019)\n<doi:10.1080/01621459.2018.1518236>). An estimate of tuning\nparameter in penalized regression is decided corresponding to\nthe lower bound of the proportion of false null hypotheses.\nDifferent penalized regression methods are provided in the\nmulti-split algorithm.",
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