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Marginal false discovery rates for penalized regression models
[article]
2017
arXiv
pre-print
Penalized regression methods are an attractive tool for high-dimensional data analysis, but their widespread adoption has been hampered by the difficulty of applying inferential tools. In particular, the question "How reliable is the selection of those features?" has proved difficult to address. In part, this difficulty arises from defining false discoveries in the classical, fully conditional sense, which is possible in low dimensions but does not scale well to high-dimensional settings. Here,
arXiv:1607.05636v2
fatcat:a5ltcoxj6fdd3ituvdprqrvm7m